This data set contains a collection of impulse responses (stored in SOFA format) from spherical microphone arrays (SMAs), equatorial microphone arrays (EMAs), and non-spherical microphone arrays (XMAs). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see DATA section for a full description).

The original purpose of the microphone array data is the binaural rendering in the spherical harmonics (SH) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, binaural room impulse responses (BRIRs) for 360 horizontal head orientations of a G.R.A.S KEMAR acoustic dummy head are provided as a reference for each scenario.

Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.

====== DATA======

This archive contains the processed impulse response sets of various measurement configurations, as described in this section.

Directory "resources/ARIR_processed/":

Post-processed SMA and EMA impulse responses

"_SMA*_" or "_EMA*_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From 1x DPA 4060 microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius

High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations):

Hall: 1202 channels (Lebedev grid) for maximum SH order 29

Others: 2702 channels (Lebedev grid) for maximum SH order 44

Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved):

SH order 29: 1742 channels (t-design grid) for SMA; 59 channels (equiangular grid) for EMA

SH order 12: 314 channels (t-design grid) for SMA; 25 channels (equiangular grid) for EMA

SH order 8: 146 channels (t-design grid) for SMA; 17 channels (equiangular grid) for EMA

SH order 4: 42 channels (t-design grid) for SMA; 9 channels (equiangular grid) for EMA

SH order 2: 14 channels (t-design grid) for SMA; 5 channels (equiangular grid) for EMA

SH order 1: 6 channels (t-design grid) for SMA; 3 channels (equiangular grid) for EMA

Post-processed XMA impulse responses

"_XMA*_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From 18x Rode Lavalier GO microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)

High-resolution data (measured simultaneously):

18 channels for maximum SH order 8

Lower-resolution data via integer subsets of microphones:

SH order 4: 9 channels

SH order 2: 6 channels

Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)

Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)

Generated XMA calibration filters and equalization filters

"_x_nm_" and "_e_nm_" in the file name

In proprietary Matlab format

Time-domain representation of filters in the respective orders of "real" spherical harmonics

Post-processed binaural impulse responses

"_KEMAR_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From G.R.A.S KEMAR dummy head with large pinna

For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)

Thereby, impulse response sets are included for five acoustic environments

"Simulation_": Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)

"Anechoic_": Anechoic measurement of a Genelec 8030A loudspeaker at the same height of the array

"LabDry_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)

"LabWet_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)

"Hall_": Room measurement in a very reverberant hall of a Genelec 8030A loudspeaker at three different source heights

Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis)

"_SrcHigh": The source is located above the horizon of the receiver

"_SrcEar": The source and receiver are located at the same height

"_SrcLow": The source is located below the horizon of the receiver

Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included

"Anechoic_MicSMAnoTape_": SMA measurement microphone without the applied tape (the source was compensated)

"Anechoic_MicSMAwithTape_": SMA measurement microphone with the applied tape (the source was compensated)

"Anechoic_MicXMAmic19_": XMA measurement microphone (the source was compensated)

"Anechoic_SrcFreeField_": Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)

"Anechoic_SrcFreeField+MicSMAnoTape_": Measurement source and SMA measurement microphone without the tape applied

"Anechoic_SrcFreeField+MicSMAwithTape_": Measurement source and SMA measurement microphone with the tape applied

"Anechoic_SrcFreeField+MicXMAmic19_": Measurement source and XMA measurement microphone

Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior):

Anechoic KEMAR: Source

Anechoic SMA/EMA/XMA: Source and array microphones

Rooms KEMAR: None

Rooms SMA/EMA/XMA: Array microphones

There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.

=========== DATA_RAW===========

This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.

The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section CODE_AND_PLOTS). The data yields the final impulse responses (see section DATA), as described in this section.

Directory "resources/ARIR_raw/":

Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data

In proprietary Matlab format, separate for every measurement position of each configuration

Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.

Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.

Directory "resources/ARIR_raw/Logs_temp_humidity/":

Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements

In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section CODE_AND_PLOTS)

This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.

================== CODE_AND_PLOTS==================

This archive contains the code required to gather the raw acoustic measurement data (see section DATA_RAW), the code to post-process and yield the final impulse response data (see section DATA), and the resulting plots as described in this section.

Directory "dependencies/":

Matlab and Python functions that are utilized in the code

Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.

Directory "plots/":

Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.

Shell script "x1_Start_Jupyter.sh":

Prepare a Python environment with the required tools described as dependencies.

Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.

Python Jupyter notebook "x1a_Measure_Microphones.ipynb":

Setup and test the utilized acoustic measurement hardware.

Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.

Export the raw acoustic data and processed impulse responses.

Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":

Setup and test the utilized acoustic measurement hardware.

Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.

Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.

Export the raw acoustic data and processed impulse responses.

Python Jupyter notebook "x1c_Measure_SMAs.ipynb":

Setup and test the utilized acoustic measurement hardware.

Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.

Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room

This data set contains a collection of impulse responses (stored in SOFA format) from spherical microphone arrays (SMAs), equatorial microphone arrays (EMAs), and non-spherical microphone arrays (XMAs). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see DATA section for a full description).

The original purpose of the microphone array data is the binaural rendering in the spherical harmonics (SH) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, binaural room impulse responses (BRIRs) for 360 horizontal head orientations of a G.R.A.S KEMAR acoustic dummy head are provided as a reference for each scenario.

Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.

====== DATA======

This archive contains the processed impulse response sets of various measurement configurations, as described in this section.

Directory "resources/ARIR_processed/":

Post-processed SMA and EMA impulse responses

"_SMA*_" or "_EMA*_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From 1x DPA 4060 microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius

High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations):

Hall: 1202 channels (Lebedev grid) for maximum SH order 29

Others: 2702 channels (Lebedev grid) for maximum SH order 44

Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved):

SH order 29: 1742 channels (t-design grid) for SMA; 59 channels (equiangular grid) for EMA

SH order 12: 314 channels (t-design grid) for SMA; 25 channels (equiangular grid) for EMA

SH order 8: 146 channels (t-design grid) for SMA; 17 channels (equiangular grid) for EMA

SH order 4: 42 channels (t-design grid) for SMA; 9 channels (equiangular grid) for EMA

SH order 2: 14 channels (t-design grid) for SMA; 5 channels (equiangular grid) for EMA

SH order 1: 6 channels (t-design grid) for SMA; 3 channels (equiangular grid) for EMA

Post-processed XMA impulse responses

"_XMA*_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From 18x Rode Lavalier GO microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)

High-resolution data (measured simultaneously):

18 channels for maximum SH order 8

Lower-resolution data via integer subsets of microphones:

SH order 4: 9 channels

SH order 2: 6 channels

Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)

Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)

Generated XMA calibration filters and equalization filters

"_x_nm_" and "_e_nm_" in the file name

In proprietary Matlab format

Time-domain representation of filters in the respective orders of "real" spherical harmonics

Post-processed binaural impulse responses

"_KEMAR_" in the file name

In SOFA format with "SingleRoomSRIR" convention

From G.R.A.S KEMAR dummy head with large pinna

For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)

Thereby, impulse response sets are included for five acoustic environments

"Simulation_": Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)

"Anechoic_": Anechoic measurement of a Genelec 8030A loudspeaker at the same height of the array

"LabDry_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)

"LabWet_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)

"Hall_": Room measurement in a very reverberant hall of a Genelec 8030A loudspeaker at three different source heights

Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis)

"_SrcHigh": The source is located above the horizon of the receiver

"_SrcEar": The source and receiver are located at the same height

"_SrcLow": The source is located below the horizon of the receiver

Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included

"Anechoic_MicSMAnoTape_": SMA measurement microphone without the applied tape (the source was compensated)

"Anechoic_MicSMAwithTape_": SMA measurement microphone with the applied tape (the source was compensated)

"Anechoic_MicXMAmic19_": XMA measurement microphone (the source was compensated)

"Anechoic_SrcFreeField_": Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)

"Anechoic_SrcFreeField+MicSMAnoTape_": Measurement source and SMA measurement microphone without the tape applied

"Anechoic_SrcFreeField+MicSMAwithTape_": Measurement source and SMA measurement microphone with the tape applied

"Anechoic_SrcFreeField+MicXMAmic19_": Measurement source and XMA measurement microphone

Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior):

Anechoic KEMAR: Source

Anechoic SMA/EMA/XMA: Source and array microphones

Rooms KEMAR: None

Rooms SMA/EMA/XMA: Array microphones

There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.

=========== DATA_RAW===========

This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.

The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section CODE_AND_PLOTS). The data yields the final impulse responses (see section DATA), as described in this section.

Directory "resources/ARIR_raw/":

Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data

In proprietary Matlab format, separate for every measurement position of each configuration

Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.

Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.

Directory "resources/ARIR_raw/Logs_temp_humidity/":

Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements

In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section CODE_AND_PLOTS)

This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.

================== CODE_AND_PLOTS==================

This archive contains the code required to gather the raw acoustic measurement data (see section DATA_RAW), the code to post-process and yield the final impulse response data (see section DATA), and the resulting plots as described in this section.

Directory "dependencies/":

Matlab and Python functions that are utilized in the code

Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.

Directory "plots/":

Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.

Shell script "x1_Start_Jupyter.sh":

Prepare a Python environment with the required tools described as dependencies.

Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.

Python Jupyter notebook "x1a_Measure_Microphones.ipynb":

Setup and test the utilized acoustic measurement hardware.

Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.

Export the raw acoustic data and processed impulse responses.

Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":

Setup and test the utilized acoustic measurement hardware.

Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.

Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.

Export the raw acoustic data and processed impulse responses.

Python Jupyter notebook "x1c_Measure_SMAs.ipynb":

Setup and test the utilized acoustic measurement hardware.

Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.

Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room

The phased array microphone market is experiencing robust growth, driven by increasing demand across diverse applications. While precise market size figures for 2025 aren't provided, considering the presence of established players like Fluke, Siemens, and Brüel & Kjær alongside emerging companies, and referencing similar technology markets with comparable growth trajectories, a reasonable estimate for the 2025 market size would be in the range of $150 million. This signifies a significant market presence, particularly given the specialized nature of phased array microphones. Assuming a Compound Annual Growth Rate (CAGR) of 12% (a conservative estimate given technological advancements and expanding applications), the market is projected to reach approximately $350 million by 2033. This growth is fueled by several key drivers: the rising need for precise sound localization in advanced acoustic imaging systems, the increasing adoption of phased array microphones in noise cancellation technologies for various industries (automotive, aerospace, consumer electronics), and expanding applications in areas like non-destructive testing and medical diagnostics. Further propelling this growth are ongoing technological innovations leading to smaller, more efficient, and cost-effective phased array microphone designs. The market's growth, however, is not without its challenges. High initial investment costs for implementing phased array microphone systems can act as a significant restraint, particularly for smaller companies or in developing regions. The complexity of signal processing and data analysis associated with phased array microphones also requires specialized expertise, hindering wider adoption. Furthermore, the availability of alternative, albeit less precise, acoustic sensing technologies presents competitive pressure. Despite these restraints, the continued innovation in signal processing techniques and the growing demand for precise acoustic measurements across multiple sectors indicate a strong outlook for the phased array microphone market over the next decade. The segments within this market— likely categorized by application (automotive, aerospace, industrial, etc.) and by microphone technology (capacitive, piezoelectric, etc.)— will likely see diverse growth rates depending on the industry's adoption speed and technological maturity.

DEMAND: Diverse Environments Multichannel Acoustic Noise Database

A database of 16-channel environmental noise recordings

Introduction

Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.

Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the AURORA-2 corpus, the CHiME background noise data, or the NOISEX-92 database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.

The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.

License

This work, the audio data and the document describing it, is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

The data

A description of the data and the recording equipment is provided in the file DEMAND.pdf. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.

Other information

The MATLAB scripts listed in the documentation can be found in the file scripts.zip.

The Authors

This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program VERSAMUS.

Road Noise Microphone Array Market Outlook

According to our latest research, the global market size for the Road Noise Microphone Array Market reached USD 1.12 billion in 2024. The market is experiencing robust expansion, with a recorded CAGR of 8.3% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 2.15 billion. This dynamic growth is primarily driven by the increasing demand for advanced noise monitoring solutions in the automotive and transportation sectors, fueled by stringent regulatory requirements and the push for quieter, more comfortable vehicles.

One of the primary growth factors for the Road Noise Microphone Array Market is the rapid advancement in sensor technologies, particularly in the automotive sector. As electric vehicles (EVs) and hybrid vehicles become more prevalent, the focus on minimizing cabin noise has intensified. Manufacturers are increasingly integrating sophisticated microphone arrays to analyze and mitigate road and tire noise, thus enhancing passenger comfort. The adoption of MEMS (Micro-Electro-Mechanical Systems) microphones, which offer superior sensitivity and reliability, has further propelled market growth. Additionally, the proliferation of smart vehicles equipped with advanced driver-assistance systems (ADAS) has necessitated the deployment of high-performance noise monitoring systems, driving sustained demand for microphone arrays.

Another significant driver is the tightening of government regulations and standards concerning vehicular noise pollution. Regulatory bodies across North America, Europe, and Asia Pacific are mandating stricter noise emission standards for both passenger and commercial vehicles. This has compelled automotive manufacturers and transportation authorities to invest heavily in advanced road noise monitoring and assessment technologies. The need for comprehensive data on environmental noise levels, especially in urban and densely populated areas, has also stimulated demand for microphone arrays in road traffic monitoring and environmental noise assessment applications. These regulatory pressures are expected to remain a key catalyst for market growth over the forecast period.

Moreover, the increasing focus on urban sustainability and smart infrastructure development has catalyzed the adoption of road noise microphone arrays in city planning and environmental noise assessment. Urban planners and research institutes are leveraging these technologies to map noise pollution hotspots, implement noise mitigation measures, and enhance the quality of urban life. The integration of microphone arrays with IoT platforms and cloud-based analytics has enabled real-time monitoring and data-driven decision-making, further expanding the market’s application scope. This trend is particularly pronounced in developed regions where smart city initiatives are gaining momentum, but is also emerging in fast-growing economies seeking to address urbanization challenges.

Regionally, Asia Pacific is emerging as the fastest-growing market for road noise microphone arrays, buoyed by rapid urbanization, expanding automotive production, and increasing investments in smart transportation infrastructure. North America and Europe continue to lead in terms of technological innovation and regulatory enforcement, while Latin America and the Middle East & Africa are gradually catching up, driven by infrastructure modernization programs. The regional dynamics are shaped by varying degrees of technological adoption, regulatory frameworks, and economic development, but the underlying trend across all regions is the growing recognition of the importance of noise monitoring in enhancing transportation safety, environmental sustainability, and public health.

Product Type Analysis

The Product Type segment of the Road Noise Microphone Array Market comprises linear microphone arrays, circular microphone arrays, spherical microphone arrays, and other configurations. Linear m

Microphone Array Tailpipe NVH Diagnosis Market Outlook

As per our latest research, the global Microphone Array Tailpipe NVH Diagnosis market size is valued at USD 672.4 million in 2024, with a robust compound annual growth rate (CAGR) of 8.1% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 1,288.7 million. The primary growth driver for this market is the increasing demand for advanced noise, vibration, and harshness (NVH) diagnostic solutions in automotive and industrial applications, driven by stringent regulatory standards and the need to enhance product quality and user experience.

One of the most significant growth factors for the Microphone Array Tailpipe NVH Diagnosis market is the tightening of global emission and noise regulations. Governments and regulatory bodies in major economies such as the United States, Germany, China, and Japan have implemented stringent standards for vehicle and industrial noise emissions. This has compelled automotive manufacturers and industrial machinery producers to adopt advanced NVH diagnostic tools, including microphone array systems, to ensure compliance. The ability of microphone arrays to provide highly accurate and spatially resolved acoustic measurements makes them indispensable for identifying and mitigating sources of noise and vibration in complex systems. As regulatory scrutiny continues to intensify, the adoption of these systems is expected to grow exponentially across various sectors.

Another key driver is the rapid technological advancement in microphone array design, signal processing algorithms, and data analytics. The integration of MEMS (Micro-Electro-Mechanical Systems) microphones, digital signal processing, and artificial intelligence has enhanced the sensitivity, accuracy, and real-time diagnostic capabilities of NVH systems. These advancements not only enable more precise identification of noise sources but also facilitate predictive maintenance and quality control in manufacturing environments. The trend towards electric and hybrid vehicles, which have unique NVH challenges compared to traditional internal combustion engine vehicles, is further accelerating the demand for sophisticated tailpipe NVH diagnosis solutions. The ongoing digital transformation in automotive and industrial sectors is expected to foster continuous innovation in this market.

The Microphone Array Tailpipe NVH Diagnosis market is also benefiting from the increasing focus on product differentiation and customer satisfaction among OEMs (Original Equipment Manufacturers) and suppliers. As end-users become more discerning about product quality, manufacturers are leveraging advanced NVH diagnostic tools to enhance the acoustic comfort and reliability of their offerings. This is particularly evident in the automotive and consumer electronics industries, where brand reputation and user experience are closely tied to NVH performance. The growing adoption of electric vehicles, smart appliances, and high-performance machinery is creating new opportunities for microphone array-based diagnostics, driving sustained market growth through the forecast period.

From a regional perspective, Asia Pacific is emerging as the dominant market for Microphone Array Tailpipe NVH Diagnosis solutions, accounting for the largest share in 2024. This growth is primarily attributed to the rapid expansion of the automotive and industrial sectors in countries such as China, India, Japan, and South Korea. North America and Europe also represent significant markets, driven by advanced manufacturing capabilities, high R&D investments, and stringent regulatory frameworks. Meanwhile, the Middle East & Africa and Latin America regions are experiencing steady growth, supported by increasing industrialization and infrastructure development. The regional dynamics are expected to evolve further as emerging economies ramp up investments in advanced diagnostic technologies to enhance product quality and competitiveness.

Product Type Analysis

The Microphone Array Tailpipe NVH Diagnosis market is segmented by product type into hardware, software, and services. The hardware segment, which includes microphone arrays, data acquisition systems, and supporting electronics, currently holds the largest share of the market. This dominance is driven by continuous advancements in sensor technology, miniaturization, and the integration of high-performance MEMS microphon

Widely varying working conditions of a space shuttle and the special design of an astronaut's spacesuit form an extreme acoustic environment that imposes unique challenges for capturing and transmitting speech communications to and from a crewmember. NASA has a serious unmet need for innovative voice communication systems and technologies, which provide enhanced speech intelligibility and quality, comfort and ease of use, and adequate hearing protection. This project will build on knowledge and recent breakthroughs produced by painstaking research at Bell Labs and WeVoice, Inc., in acoustic and speech signal processing for hands-free communications. It brings together the state-of-the-art and patent-pending techniques in microphone arrays, speech enhancement, and active noise control, and proposes an integrated, more reliable solution for combating high-level noise and strong reverberation. This proof-of-feasibility research will focus primarily on whether the proposed techniques that were previously developed for applications in room acoustic environments can perform as well as or better than we expect in an in-helmet acoustic environment. In addition, this research will use informal listening tests to demonstrate performance improvement and will design a subjective program that can be readily executed in Phase II to rigorously evaluate the overall system performance. The Phase I effort will provide a foundation for prototype design to be conducted in Phase II.

Data availability is essential to develop acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse training datasets. For this reason, an increasing number of databases have been published in recent years, including either room impulse responses (RIRs) or audio recordings during motion. In this paper we introduce the trajectoRIR database, an extensive, multi-array collection of both dynamic and stationary acoustic recordings along a controlled trajectory in a room. Specifically, the database features recordings using moving microphones and stationary RIRs spatially sampling the room acoustics along an L-shaped trajectory. This combination makes trajectoRIR unique and applicable in various tasks ranging from sound source localization and tracking to spatially dynamic sound field reconstruction, auralization and system identification. The recording room has a reverberation time of 0.5s, and the three different microphone configurations employed include a dummy head, with additional reference microphones located next to the ears, 3 first-order Ambisonics microphones, two circular arrays of 16 and 4 channels, and a 12-channel linear array. The motion of the microphones was achieved using a robotic cart traversing a 4.62m-long rail at three speeds: [0.2, 0.4, 0.8] m/s. Audio signals were reproduced using two stationary loudspeakers. The collected database features 8648 stationary RIRs, as well as perfect sweeps, speech, music, and stationary noise recorded during motion. Python functions are included to access the recorded audio as well as to retrieve geometrical information.

For a detailed description, please refer to the paper: preprint

Publication

If you use this database, please cite the following paper:

S. Damiano, K. MacWilliam, V. Lorenzoni,. T. Dietzen and T. van Waterschoot, "The trajectoRIR Database: Room Acoustic Recordings Along a Trajectory of Moving Microphones,” arXiv:2503.23004, 2025. DOI: https://doi.org/10.48550/arXiv.2503.23004

DESCRIPTION

This dataset includes acoustic impulse response (AIR) measurements made using an Eigenmike em32 and the room impulse response measurements carried out at the same position using an Alctron M6 measurement microphone. The measurements were made in classroom S05 at the METU Graduate School of Informatics on 23 January 2018.

LICENSE

The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

MEASUREMENTS

The classroom in which the measurements were made has a high reverberation time (T60 ≈ 1.12 s) when empty. The room is approximately rectangular and has the dimensions 6.5 × 8.3 × 2.9 m. AIR measurements were made at 240 points on a rectilinear grid of 0.5 m horizontal and 0.3 m vertical resolution surrounding the array. The array was positioned at a height of 1.5 m. The measurement planes were positioned at the heights of 0.9, 1.2, 1.5, 1.8 and 2.1 m from the floor level. These positions cover the whole azimuth range and an elevation range of approximately ±50◦ above and below the horizontal plane.

The sound source was a Genelec 6010A two-way loudspeaker whose acoustic axis pointed at the vertical axis of the array at all measurement positions. Logarithmic sine sweep method was used for the AIR measurements.

FILE FORMAT

The AIRs and RIRs are provided as 16-bit signed integer WAVE files. The sampling rate is 48 kHz.

NAMING CONVENTION

There are two folders: em32 and alctron. The former includes AIR measurements, and the latter includes the RIR measurements. Each of these folders include 244 subfolders where each subfolder is named as ABC, from 000 through to 664. See documentation.pdf for details.

Note that the measurements right above and below the array are not ideal since the acoustic axis of the loudspeaker did not face the array. Therefore, these measurements are considered unfit and were not used in the publications given below.

HOW TO CITE

The dataset has the DOI number 10.5281/zenodo.2635758 and can be cited as:

Orhun Olgun, & Huseyin Hacihabiboglu. (2019). METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0 (Version 0.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2635758

The data presented here were used in one journal article and two conference papers as of the time of writing this document. Please also consider citing these papers if you find this dataset to be useful in your research:

[1] Coteli, M. B., Olgun, O., and Hacihabiboglu, H. (2018). Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement. IEEE/ACM Trans. Audio, Speech and Language Process., 26(11), 2215-2229.

[2] Olgun, O. and Hacihabiboglu, H., (2018) "Localization of Multiple Sources in the Spherical Harmonic Domain with Hierarchical Grid Refinement and EB-MUSIC". In Proc. 2018 16th Int. Workshop on Acoust. Signal Enhancement (IWAENC-18) (pp. 101-105), Tokyo, Japan.

[3] Coteli, M. B., and Hacihabiboglu, H., (2019), "Multiple Sound Source Localization with Rigid Spherical Microphone Arrays Via Residual Energy Test", Proc. 2019 IEEE Int. Conf. on Acoust., Speech and Signal Process., (ICASSP-19), Brighton, UK.

Species of open-space bats that are relatively large, such as bats from the genus Nyctalus, are considered as high-risk species for collisions with wind turbines. However, important information on their behavior and movement ecology, such as the locations and altitudes at which they forage, is still fragmentary, while crucial for their conservation in light of the increasing threat posed by progressing wind turbine construction. We adopted two different methods of microphone array recordings and GPS-tracking capturing data from different spatio-temporal scales in order to gain a complementary understanding of the echolocation and movement ecology of Nyctalus aviator, the largest open-space bat in Japan. Based on microphone-array recordings, we found that echolocation calls during natural foraging are adapted for fast-flight in open space optimal for aerial-hawing. In addition, we attached a GPS tag that can simultaneously monitor feeding buzz occurrence and confirmed that foraging occurred at 300 m altitude and that the flight altitude in mountainous areas is consistent with the turbine conflict zone. Thus, our acoustic GPS survey clearly identified N. aviator as a high-risk species in Japan. Methods Microphone-array recordings Microphone array recordings were conducted on a total of 10 days in August 2020 and 2021: August 4 and 5, 2020, June 26, 27, 30, July 2, 28, 30, August 1 and 2, 2021 (total 1210 min), at Tokiwa Park in Asahikawa city, Hokkaido, Japan (N43°46'28.6" E142°21'27.1"), in the proximity of N. aviator roosts within hollows of Ulmus davidiana. The measurement was started around 21:00 in 2020 and around sunset (approximately 19:00) in 2021, and the recordings were made for about 2–3 hours for each day until the batteries ran out. A Y-shaped microphone array, consisting of four omnidirectional microphones (FG-23329-C05; Knowles Electronics, Itasca, IL), was placed at a height of approximately 1 m above the ground with the microphone tips pointing upwards. Based on the difference in arrival times between a central and three outer microphones, the 3D locations of flying bats were reconstructed using a custom made program in Matlab (Math Works, Natick, MA, USA). The detailed procedure of recording and calculation of the 3D positions is described in Fujioka et al 2011 and Mizuguchi et al 2022. The coordinates of the bats were calculated within a range of 47 m from the microphone array where the theoretical range error was less than 40 cm (corresponding to wing length of N. aviator). Those echolocation calls that were recorded by the central microphone with a sufficiently high signal to noise ratio were further analyzed. The end and start times of the pulses were automatically obtained based on a -15dB threshold from the peak power. Then, the inter pulse interval (IPI), which is the time between the beginning of a pulse and the end of the next pulse, the pulse duration, the minimum frequency, the bandwidth and the peak frequency were calculated using a custom-made program in Matlab, respectively. We identified feeding activity based on a characteristic sequence of echolocation calls termed “feeding buzz” from the spectrogram.
Acoustic-GPS logging We caught bats using mist nets close to a roosting tree (Ulmus davidiana) next to Asahikawa elementary school in eastern Asahikawa city, central Hokkaido, Japan (N43°46'10.0" E142°26'27.5") during sunset on July 29th, 2021. Then, we carefully attached custom-made acoustic GPS data loggers (ArumoTech Corp., Kyoto, Japan, 2.4g) with a telemetry unit (PicoPip Ag337, Lotek, Canada, 0.3g) using Skin Bond (Osto-bond, Montreal Ostomy Inc., Canada) to the back of the bat. We held the bats for about 10 min to allow the glue to dry and released them on the roosting tree. The timers were set to start GPS logging every 5 seconds from 20:00 o’clock on the second day after the attachment until batteries ran out (approximately 1 hour). It is possible to continuously record pulse emission timing with a high temporal resolution of 4 MHz sampling rate, because acoustic GPS loggers are designed to output a high voltage when the acoustic signal voltage exceeds a certain threshold. We identified feeding buzzes from the recorded IPI patterns based on observations from the microphone array recordings. In particular, pulse sequences with at least five pulses below an IPI threshold of 20 ms and a maximum duration of 100 ms were automatically classified as a feeding buzz (attack). The automatic classification of feeding buzzes corresponded to 100% of those that we visually found in the IPI sequences. We identified the coordinates of the attack location by resampling GPS data from 5 seconds to every second using linear completion, and searching for the timing closest to the end of a feeding buzz. The flight altitude was obtained by subtracting the ground altitude (the Geospatial Information Authority of Japan, https://www.gsi.go.jp/ENGLISH/index.html) from the absolute altitude above sea level recorded by the acoustic GPS logger. We identified habitat types (river, mountain, urban) from 'topographic' and 'satellite' maps provided in MATLAB. Statistical Analysis All statistical analyses were performed in the R environment for statistical computing (R Core Team 2021) and its extended packages. We modeled the flight speed, altitude as well as the presence/absence of an attack, as a function of the habitat type (river, mountain, urban area) using linear modeling, generalized liner modeling [lme4, version 1.1-28, 29] and generalized linear mixed modeling using Template Model Builder [package glmmTMB_1.1.4, 30], respectively, to examine whether the tagged bat adapted its flight and foraging behavior to the respective habitat type. In general, we examined the quality of a model fit graphically using the functions in the DHARMa package [version 0.3.3.0, 31] We checked whether the model explained more variance than its respective null model by comparing them either via a parametric bootstrapping method [package pbkrtest_0.5.1, 32] or via a χ2 test (function anova, R 2022). A χ2 type-II-Wald test [function Anova, package car_3.0.12, 33] was used to check the significance of the factor within the respective model. Bonferroni correction was applied to all pairwise post-hoc comparisons between the levels the factor (function lsmeans, package emmeans_1.8.0). In the residuals of the linear model for the flight speed, we detected heteroscedasticity which was most probably caused by the strong variance in the data and potentially also by the unbalanced number of data points between habitat categories. To correct the model, we subsampled the data by randomly selecting 76 data points from each habitat category. The residuals of the first model for flight altitude indicated temporal autocorrelation as well as heteroscedasticity. We corrected this model by randomly subsampling the data to a number of 40 data points per habitat type and applying a glmmTMB model with a Gaussian error distribution and the factor for habitat type as the dispersion parameter. Finally, we analyzed whether the attack probability differed between habitat types by modeling the presence and absence of an attack in a generalized model with binomial error distribution. For this model, we were able to use the full amount of data without any subsampling.

Background

Acoustic telemetry is now a key research tool used to quantify juvenile salmon survival, but transmitter size has limited past studies to larger smolts (>130 mm fork length). New, smaller, higher-frequency transmitters ("tags") allow studies on a larger fraction of the smolt size spectrum (>95 mm); however, detection range and study duration are also reduced, introducing new challenges. The potential cost implications are not trivial. With these new transmitters in mind, we designed, deployed, and tested the performance of a dual-frequency receiver array design in the Discovery Islands region of British Columbia, Canada. We double-tagged 50 juvenile steelhead (Oncorhynchus mykiss) with large 69 kHz tags (VEMCO model V9-1H) and small 180 kHz tags (model V4-1H). The more powerful 69 kHz tags were used to determine fish presence in order to estimate the detection efficiency (DE) of the 180 kHz tags. We then compared the standard error of the survival estimate produced from the tracking data using the two tag types which has important implications for array performance and hypothesis testing in the sea.

Results

Perfect detection of the 69 kHz tags allowed us to determine the DE of the 180 kHz tags. Although the 180 kHz tags began to expire during the study, the estimated DE was acceptable at 76% (SE = 9%) when we include single detections. However, confidence intervals on steelhead survival (64%) were 1.5x larger for the 180 kHz tags (47-85% vs. 51-77% for 69 kHz) because of the reduced DE.

Conclusions

The array design performed well; however, single detections of the 180 kHz tags indicates that under slightly different circumstances the DE could have been compromised, emphasizing the need to carefully consider the interaction of animal migration characteristics, study design, and tag programming when designing telemetry arrays. To increase DE and improve the precision of 180 kHz-based survival estimates presented here requires either an increase in receiver density, an increase in tag sample size (and modified transmitter programming), or both. The optimal solution depends on transmitter costs, array infrastructure costs, annual maintenance costs, and array use (i.e., contributors). Importantly, the use of smaller tags reduces potential tag burden effects and allows early marine migration studies to be extended to Pacific salmon populations that have been previously impossible to study.

Explore the dynamic Array Microphone market, projected for substantial growth driven by advanced audio technology in events, conferences, and large halls. Discover key drivers, trends, and regional insights.

The global handheld acoustic camera market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 are not explicitly provided, a reasonable estimation can be made based on industry trends and publicly available data from similar technologies. Considering the adoption rate of acoustic cameras in noise reduction, predictive maintenance, and quality control processes, a conservative estimate places the 2025 market size at approximately $250 million. This figure is further supported by the significant investments being made by key players in research and development, along with the expansion of their product portfolios to cater to specific application needs. We project a Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033), reflecting the increasing awareness of the benefits of acoustic imaging and the technological advancements leading to more affordable and user-friendly handheld acoustic cameras. This growth is being propelled by several factors, including the rising adoption of Industry 4.0 principles, the need for enhanced predictive maintenance strategies to reduce downtime and operational costs, and the stringent regulations on noise pollution in various industries. The market segmentation reveals a strong preference for 2D microphone arrays, which currently dominate the market share. However, the 3D microphone array segment is poised for significant growth in the coming years due to its advanced capabilities in providing more precise and detailed acoustic information. Across applications, the manufacturing, automotive, and energy sectors are leading adopters, fueled by their need for efficient troubleshooting and quality control. However, the building and infrastructure, and aerospace sectors are showing increasing potential, indicating substantial growth prospects in these areas. Geographically, North America and Europe currently hold the largest market share, but the Asia-Pacific region is expected to witness rapid growth due to its expanding industrial base and increasing investments in technological advancements. The competitive landscape is relatively concentrated, with several prominent players vying for market leadership through product innovation and strategic partnerships. This dynamic environment ensures continued innovation and further penetration into newer markets, driving market expansion in the years to come.

DESCRIPTION

The TAU Spatial Room Impulse Response Database (TAU-SRIR DB) database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:

• Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).
• Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.
• Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.
• Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.

The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.

NOTE: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.

REPORT AND REFERENCE

A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:

Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan.

available here. A more detailed report specifically focusing on the dataset collection and properties will follow.

AIM

The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:

• monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions
• monophonic and multichannel polyphonic sound events in multi-room reverberant conditions
• single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios
• single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios
• sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios

SPECIFICATIONS

The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.

The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:

1. Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.
2. Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.
3. Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.
4. Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.
5. Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.
6. Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.
7. Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.
8. Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.
9. Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.

The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a close and a far one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, close and far, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.

Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights.

The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.

More details on the trajectory geometries can be found in the README file and the measinfo.mat file.

RECORDING FORMATS

As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README.

We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).

REFERENCE DOAs

For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.

The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.

SCENE GENERATOR

A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the TAU-NIGENS Spatial Sound Events 2021 dataset, and has been ported to Python from the original version written in Matlab.

The generator can be found [**here**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use.

The generator at the moment is set to work with the NIGENS sound event sample database, and the FSD50K sound event database, but additional sample banks can be added with small modifications.

The dataset together with the generator has been used by the authors in the following public challenges:

- DCASE 2019 Challenge Task 3, to generate the TAU Spatial Sound Events 2019 dataset (development/evaluation)

- <a

A database of 16-channel environmental noise recordings

Introduction

Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.

Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the AURORA-2 corpus, the CHiME background noise data, or the NOISEX-92 database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.

The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.

The data

A description of the data and the recording equipment is provided in the file DEMAND.pdf. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.

The Authors

This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program VERSAMUS.

Sound Camera Acoustic Array Portable Market Outlook

As per our latest research, the global Sound Camera Acoustic Array Portable market size in 2024 stands at USD 392.7 million, with a robust growth trajectory driven by increasing adoption across diverse industrial sectors. The market is expected to grow at a CAGR of 12.4% from 2025 to 2033, reaching a forecasted value of USD 1,134.2 million by 2033. This impressive growth is primarily attributed to the rising demand for advanced acoustic imaging solutions in industrial inspection, environmental monitoring, and research applications, coupled with ongoing technological advancements in microphone array technologies and data processing capabilities.

One of the key growth factors propelling the sound camera acoustic array portable market is the increasing emphasis on predictive maintenance and asset management in manufacturing and industrial environments. As industries worldwide strive to minimize downtime and improve operational efficiency, the adoption of portable sound cameras has surged. These devices enable rapid detection and localization of noise sources, facilitating early identification of equipment faults, air leaks, and mechanical anomalies. The portability and ease of deployment of these acoustic arrays allow for flexible, on-the-spot diagnostics, which is particularly valuable in complex industrial settings where traditional inspection methods are time-consuming and less effective. Additionally, the integration of advanced analytics and real-time data visualization has made these solutions even more attractive to end users seeking actionable insights for maintenance and quality assurance.

Another significant driver is the growing focus on environmental noise monitoring and compliance with stringent regulatory standards. Urbanization, infrastructure development, and increased public awareness of noise pollution have led to heightened demand for accurate acoustic mapping tools. Portable sound camera arrays, with their ability to provide spatially resolved noise measurements, are being widely adopted by environmental agencies, urban planners, and construction companies. These tools are instrumental in identifying noise hotspots, assessing the impact of construction activities, and ensuring adherence to local and international noise regulations. The versatility of these devices, combined with their ability to generate visual representations of sound, is revolutionizing the way organizations approach environmental noise assessment and mitigation.

Technological advancements in microphone array design and signal processing algorithms are further fueling market growth. The evolution from traditional condenser microphone arrays to advanced MEMS (Micro-Electro-Mechanical Systems) microphone arrays has resulted in significant improvements in sensitivity, spatial resolution, and device miniaturization. These innovations have expanded the application scope of portable sound cameras beyond industrial and environmental monitoring to sectors such as automotive, aerospace, and research and development. Enhanced user interfaces, wireless connectivity, and integration with cloud-based platforms are also contributing to the growing adoption of these devices. As manufacturers continue to invest in R&D to enhance the performance and affordability of portable acoustic arrays, the market is poised for sustained expansion over the forecast period.

From a regional perspective, North America currently dominates the global sound camera acoustic array portable market, driven by the presence of leading technology providers, strong industrial infrastructure, and high awareness of advanced inspection tools. However, the Asia Pacific region is expected to exhibit the fastest growth during the forecast period, fueled by rapid industrialization, increasing investments in smart manufacturing, and rising adoption of acoustic monitoring solutions in emerging economies such as China, India, and Southeast Asian countries. Europe also represents a significant market share, supported by stringent environmental regulations and a strong focus on research and innovation in acoustics. The Middle East & Africa and Latin America, while currently smaller in market size, are projected to witness steady growth as awareness and adoption of portable acoustic imaging technologies expand.

Siren Localization using Microphone Arrays Market Outlook

As per our latest research, the global Siren Localization using Microphone Arrays market size in 2024 stands at USD 1.16 billion, reflecting robust adoption across smart city and transportation sectors. The market is experiencing a strong growth momentum with a CAGR of 14.2% projected over the forecast period. By 2033, the market is expected to reach USD 3.36 billion. Key growth drivers include the rising need for real-time emergency response, advancements in acoustic signal processing, and the proliferation of smart urban infrastructure. The integration of artificial intelligence and IoT technologies further catalyzes the expansion of this innovative market.

The primary growth factor for the Siren Localization using Microphone Arrays market is the increasing demand for efficient emergency response systems in densely populated urban environments. As cities become more congested, the rapid identification and localization of emergency vehicle sirens have become essential for reducing response times and improving public safety. Microphone arrays equipped with advanced signal processing algorithms enable precise localization of sirens, allowing traffic management systems to dynamically adjust traffic signals and clear paths for emergency vehicles. This not only enhances the efficiency of emergency services but also minimizes collateral risks associated with delayed response. The expansion of smart city initiatives worldwide, particularly in North America, Europe, and Asia Pacific, has further fueled the deployment of these technologies in urban infrastructure.

Another significant factor propelling the growth of the Siren Localization using Microphone Arrays market is the technological advancements in array design and machine learning-based localization methods. The incorporation of machine learning algorithms has revolutionized the accuracy and reliability of siren detection, even in noisy urban environments. These systems can distinguish between different types of sirens, filter out background noise, and provide real-time location data to law enforcement and traffic authorities. The integration with other smart city components, such as surveillance cameras and traffic management platforms, creates a holistic approach to urban safety and mobility. Furthermore, the declining cost of high-quality microphones and the development of scalable, modular array solutions have made these systems more accessible to municipalities and private operators alike.

The growing emphasis on urban traffic management and public safety is also driving market expansion. Urbanization trends have led to increased traffic congestion and a higher incidence of road accidents, necessitating the deployment of advanced monitoring and management tools. Siren localization systems play a critical role in optimizing traffic flow during emergencies, enabling authorities to implement dynamic rerouting and prioritize emergency vehicle movement. The adoption of these systems by transportation authorities, law enforcement agencies, and smart city operators is expected to continue rising, supported by favorable government regulations and funding for smart infrastructure projects. The market is further benefiting from collaborations between technology providers, municipal governments, and research institutions, which are fostering innovation and accelerating the deployment of next-generation localization solutions.

From a regional perspective, North America currently dominates the Siren Localization using Microphone Arrays market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of advanced urban infrastructure, high adoption of smart city technologies, and significant investments in public safety initiatives have positioned North America as a leader in this market. Europe is also witnessing substantial growth, driven by stringent regulations on emergency response and increasing deployment of smart transportation systems. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid urbanization, government-led smart city projects, and the need to address traffic congestion in megacities. Latin America and the Middle East & Africa are gradually catching up, with pilot projects and increasing awareness of the benefits of siren localization technologies.

Technology Analysis

The technology segment in the Siren Localization using Microphone Arrays market is characterized by a

Background High-density oligonucleotide arrays (HDONAs) are a powerful tool for assessing differential mRNA expression levels. To establish the statistical significance of an observed change in expression, one must take into account the noise introduced by the enzymatic and hybridization steps, called type I noise. We undertake an empirical characterization of the experimental repeatability of results by carrying out statistical analysis of a large number of duplicate HDONA experiments. Results We assign scoring functions for expression ratios and associated quality measures. Both the perfect-match (PM) probes and the differentials between PM and single-mismatch (MM) probes are considered as raw intensities. We then calculate the log-ratio of the noise structure using robust estimates of their intensity-dependent variance. The noise structure in the log-ratios follows a local log-normal distribution in both the PM and PM-MM cases. Significance relative to the type I noise can therefore be quantified reliably using the local standard deviation (SD). We discuss the intensity dependence of the SD and show that ratio scores greater than 1.25 are significant in the mid- to high-intensity range. Conclusions The noise inherent in HDONAs is characteristically dependent on intensity and can be well described in terms of local normalization of log-ratio distributions. Therefore, robust estimates of the local SD of these distributions provide a simple and powerful way to assess significance (relative to type I noise) in differential gene expression, and will be helpful in practice for improving the reliability of predictions from hybridization experiments.

The emergence of continental to global scale biodiversity data has led to growing understanding of patterns in species distributions, and the determinants of these distributions, at large spatial scales. However, identifying the specific mechanisms, including demographic processes, and determining species distributions remains difficult, as large-scale data are typically restricted to observations of only species presence. New remote automated approaches for collecting data, such as automated recording units (ARUs), provide a promising avenue towards direct measurement of demographic processes, such as reproduction, that cannot feasibly be measured at scale by traditional survey methods. In this study, we analyze data collected by ARUs from 452 survey points across an approximately 1500 km study region to compare patterns in adult and juvenile distributions in the Great Horned Owl (Bubo virginianus). We specifically examine whether habitat associated with successful reproduction is the ..., Owl surveys: Nighttime autonomous acoustic recordings were collected from 452 survey locations across 1500 km of the eastern United States. Two Convolutional Neural Networks were developed to classify the adult song and juvenile begging call of the Great Horned Owl (Bubo virginianus). These classifiers were run on the recordings and the highest scoring ten five-second clips occurring on ten separate days at each survey location were extracted. These clips were manually reviewed by a human listener to ensure they contained the relevant owl sounds. Presence/absence was translated into 1/0 detection histories to be used in occupancy models. Covariates: GPS coordinates were collected at each survey location (these are not provided to protect landowner identity). National Land Cover Database information was extracted for the amount of forest and agricultural land cover within a 1750 m radius of each survey location for use as occupancy covariates. Tree basal area and < 10 cm DBH stem dens..., , # Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large scale automated acoustic array

https://doi.org/10.5061/dryad.5hqbzkhcz

Description of the data and file structure

Data are provided as a single CSV file owl_data.csv with columns

• site_number (1-452 denoting unique survey locations),
• survey_number (1-10 denoting the survey number in a sequence of 10),
• song_detections (1 or 0 indicating presence or absence of Great Horned Owl song),
• beg_detections (1 or 0 indicating the presence or absence of Great Horned Owl begging calls),
• f_cover_1750m (the amount of forest within a 1750 m radius of the survey location, centered and scaled),
• f_cover_250m (the amount of forest within a 250 m radius of the survey location, centered and scaled),
• ag_cover_1750m (the amount of agricultural land cover within a 1750 m radius of the survey locat...

Short description All measurements were done with an open microphone array on a robotic platform (see figure Robot_with_array.jpg ) in an ITU-R BS.1116 conform listening room.

With consecutively adding walls (2.5m high, 1.6cm thick, painted pressboard), we intended to change the reflection patterns. All room configurations are shown in figure Roomconfigurations.jpg and Floor_plan.jpg

The robot measured the rooms according to an uniform grid (25cm spacing), always facing the same direction (north, x-direction, see figure Floor_plan.jpg). However, the real position and orientation sometimes deviate from the intended values. The real position is saved in the sofa files. The intended XY position is included in the file name.

We cannot guarantee, that all measurements are error-free. Feel free to give us feedback about problems you encounter.

The robot recorded 3 sine-sweeps for each position from each speaking (15 overall). In this dataset only one preselected SRIR per speaker and measuring position is included. If you would like to use all impulse response measurements or pure recordings, please contact us.

File format:The data is sorted according to the room configurations. Each zip file contains the data for one room configuration.

The actual data is provided in SOFA file format. The name of the sofa files consists of the room identifier and the x and y coordinates of the measured position (intended value). Each SOFA file contains the SRIRs for all five loudspeakers including metadata.

The "SOFA Toolbox v2.1" was used to generate the sofa files. The data is available in the SOFAConvention “SingleRoomSRIR” in Version 1.0

See: https://www.sofaconventions.org/