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.

This noise database was developed to provide researchers and other interested stakeholders with noise measurement results that the National Institute for Occupational Safety and Health (NIOSH) has collected during health hazard evaluation (HHE) surveys from 1996 through 2013. HHEs are requested by employees or their representatives, or employers, to help learn whether health hazards are present at their workplace. The scope of HHEs varies based on the requestors’ concerns and the NIOSH project officers’ professional judgment. Only noise measurement results are included in this database; however, many HHEs include evaluation of exposures other than noise. Individual HHE reports are published on the NIOSH website. When available, the database provides a direct link to the HHE report for each of the noise measurement results.

The noise database contains workplace noise measurement results from 77 HHE reports, including over 808 personal noise exposure measurements and 582 area noise measurements. It also includes the following information: U.S. state or territory; Occupational Safety and Health Administration (OSHA) region; National Occupational Research Agenda (NORA) sector; North American Industry Classification System (NAICS) code; facility description; type of dosimeter or sound level meter used; whether a hearing conservation program was in place; whether a hearing protection was used; whether octave band data was collected; job title; noise-generating activities; location of noise measurements; start and end date for site visit; type (full-shift, partial-shift, or task-based) and duration of noise measurement; type of noise (continuous, impulsive, or intermittent); exposure to ototoxic chemicals; and results in decibels A-weighted (dBA) and percent dose according to OSHA and NIOSH noise measurement criteria. This database is an ongoing project and will be updated at least yearly to add the most recent HHE noise measurement data.

This database includes 12 half-hour ECG recordings and 3 half-hour recordings of noise typical in ambulatory ECG recordings. The noise recordings were made using physically active volunteers and standard ECG recorders, leads, and electrodes; the electrodes were placed on the limbs in positions in which the subjects’ ECGs were not visible.

This noise database was developed to provide researchers and other interested stakeholders with noise measurement results that the National Institute for Occupational Safety and Health (NIOSH) has collected during health hazard evaluation (HHE) surveys from 1996 through 2013. HHEs are requested by employees or their representatives, or employers, to help learn whether health hazards are present at their workplace. The scope of HHEs varies based on the requestors’ concerns and the NIOSH project officers’ professional judgment. Only noise measurement results are included in this database; however, many HHEs include evaluation of exposures other than noise. Individual HHE reports are published on the NIOSH website. When available, the database provides a direct link to the HHE report for each of the noise measurement results.

The noise database contains workplace noise measurement results from 77 HHE reports, including over 808 personal noise exposure measurements and 582 area noise measurements. It also includes the following information: U.S. state or territory; Occupational Safety and Health Administration (OSHA) region; National Occupational Research Agenda (NORA) sector; North American Industry Classification System (NAICS) code; facility description; type of dosimeter or sound level meter used; whether a hearing conservation program was in place; whether a hearing protection was used; whether octave band data was collected; job title; noise-generating activities; location of noise measurements; start and end date for site visit; type (full-shift, partial-shift, or task-based) and duration of noise measurement; type of noise (continuous, impulsive, or intermittent); exposure to ototoxic chemicals; and results in decibels A-weighted (dBA) and percent dose according to OSHA and NIOSH noise measurement criteria. This database is an ongoing project and will be updated at least yearly to add the most recent HHE noise measurement data.

The Isolated urban sound database contains the audio samples used to design urban sound mixtures using SimScene software.

This database has already been used to design urban sound mixtures that can be found in Estimation of the road traffic sound levels based on Non-Negative Matrix Factorization dataset [1] and in Realistic urban sound mixture dataset [2]

The dataset contains two folders :

- 'event' which includes includes 231 brief sound samples considered as salient, with a 1 to 20 seconds duration and classified among 21 sound classes (ringing bell, whistling bird, car horn, passing car, hammer, barking dog, siren, footstep, metallic noise, voice...)

- 'background' which includes 162 long duration sounds (~1mn30), whose acoustic properties do not vary in time. This category includes among others, whistling bird, crowd noise, rain, children playing in schoolyard, constant traffic noise ...

More details on this sound database can be found in [3]

[1] J.-R. Gloaguen, M. Lagrange, A. Can, J.-F. Petiot, Estimation of the road traffic sound levels in urban areas based on non-negative matrix factorization techniques, submitted for publication

[2] J.-R. Gloaguen, A. Can, M. Lagrange, J.-F. Petiot, Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization, submitted for publication

[3] J.-R. Gloaguen, A. Can, M. Lagrange, J.-F. Petiot, Creation of a corpus of realistic urban sound scenes with controlled acoustic properties, in: Acoustics ’17 Boston, Vol. 141 of The Journal of the Acoustical Society of America, Acoustical Society of America and the European Acoustics Association, Boston, United States, 2017, pp. 4044–4044.

The QUT-NOISE Databases and Protocols

Overview

This distribution contains the QUT-NOISE database and the code required to create the QUT-NOISE-TIMIT database from the QUT-NOISE database and a locally installed copy of the TIMIT database. It also contains code to create the QUT-NOISE-SRE protocol on top of an existing speaker recognition evaluation database (such as NIST evaluations). Further information on the QUT-NOISE and QUT-NOISE-TIMIT databases is available in our paper:

D. Dean, S. Sridharan, R. Vogt, M. Mason (2010) , in Proceedings of Interspeech 2010, Makuhari Messe International Convention Complex, Makuhari, Japan.

This paper is also available in the file: docs/Dean2010, The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithm.pdf, distributed with this database.

Further information on the QUT-NOISE-SRE protocol is available in our paper:
D. Dean, A. Kanagasundaram, H. Ghaemmaghami, M. Hafizur, S. Sridharan (2015) . In Proceedings of Interspeech 2015, September, Dresden, Germany.

Licensing

The QUT-NOISE data itself is licensed CC-BY-SA, and the code required to create the QUT-NOISE-TIMIT database and QUT-NOISE-SRE protocols is licensed under the BSD license. Please consult the approriate LICENSE.txt files (in the code and QUT-NOISE directories) for more information. To attribute this database, please include the following citation:

D. Dean, S. Sridharan, R. Vogt, M. Mason (2010) , in Proceedings of Interspeech 2010, Makuhari Messe International Convention Complex, Makuhari, Japan.

If your work is based upon the QUT-NOISE-SRE, please also include this citation:
D. Dean, A. Kanagasundaram, H. Ghaemmaghami, M. Hafizur, S. Sridharan (2015) . In Proceedings of Interspeech 2015, September, Dresden, Germany.

Download and Installation

Download the following QUT-NOISE*.zip files:

• (26.7 MB, md5sum: 672461fd88782e9ea10d5c2cb7a84196)
• (1.6 GB, md5sum: f87fb213c0e1c439e1b727fb258ef2cd)
• (1.7 GB, md5sum: d680118b4517e1257a9263b99d1ac401)
• (1.4 GB, md5sum: d99572ae1c118b749c1ffdb2e0cf0d2e)
• (1.4 GB, md5sum: fe107ab341e6bc75de3a32c69344190e)
• (1.6 GB, md5sum: 68d5ebc2e60cb07927cc4d33cdf2f017)

Creating QUT-NOISE-TIMIT

Obtaining TIMIT

In order to construct the QUT-NOISE-TIMIT database from the QUT-NOISE data supplied here you will need to obtain a copy of the TIMIT database from the . If you just want to use the QUT-NOISE database, or you wish to combine it with different speech data, TIMIT is not required.

Creating QUT-NOISE-TIMIT

• Once you have obtained TIMIT, download and install a copy of and install it in your MATLABPATH.
• Run matlab in the QUT-NOISE/code directory, and run the function: createQUTNOISETIMIT('/location/of/timit-cd/timit'). This will create the QUT-NOISE-TIMIT database in the QUT-NOISE/QUT-NOISE-TIMIT directory.
• If you wish to verify that the QUT-NOISE-TIMIT database matches that evaluated in our original paper, please check that the md5sums (use md5sum on unix-based OSes) match those in the QUT-NOISE-TIMIT/md5sum.txt file.
• Using the QUT-NOISE-SRE protocol
  • The code related to the QUT-NOISE-SRE protocol can be used in two ways:
    1. To create a collection of noisy audio files across the scenarios in the QUT-NOISE database at different noise levels, or,
    2. To recreate a list of file names based on the QUT-NOISE-SRE protocl produced by another researcher, having already done (1). This allows existing research to be reproduced without having to send large volumes of audio around.
  • If you are interested in creating your own noisy database from an existing SRE database (1 above), please look at the example script exampleQUTNOISESRE.sh in the QUT-NOISE/code directory. You will need to make some modifications, but it should give you the right idea.
  • If you are interested in creating our QUT-NOISE-NIST2008 database published at Interspeech 2015, you can find the list of created noisy files in the QUT-NOISE-NIST2008.train.short2.list and QUT-NOISE-NIST2008.test.short3.list files in the QUT-NOISE/code directory.
  • These files can be recreated as follows (provided you have access to the NIST2008 SRE data):
    
    Run matlab in the QUT-NOISE/code directory, and run the following functions:
    
    createQUTNOISESREfiles('NIST2008.train.short2.list', ...
    'QUT-NOISE-NIST2008.train.short2.list', ...
    '

A series of nozzle chevrons were designed to create a parametric family with varying length, penetration and width, with the objective of demonstrating noise reduction for supersonic nozzles. Eight sets of chevrons were fabricated and tested on the High-Flow Jet Exit Rig in the Aero-Acoustic Propulsion Lab at the NASA Glenn Research Center in 2009. Details of the test are given in the paper "An MDOE Investigation of Chevrons for Supersonic Jet Noise Reduction" by Henderson and Bridges (DOI: 10.2514/6.2010-3926). This data repository contains a test requirements document with configuration and flow definitions, a spreadsheet with measured jet flow conditions from the test, chevron geometry files in CAD format, and a set of files containing spectral directivity measurements of the acoustic far-field at the ~350 test points. Details about the data files are contained in a README document.

1.44 GB; md5sum: fe107ab341e6bc75de3a32c69344190e QUT Research Data Respository Dataset Resource available for download

The 2018 National Transportation Noise Map dataset utilized transportation mode input data from 2018 in a model and is current as of October 2020, published by the Bureau of Transportation Statistics (BTS), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Please see the website https://www.bts.gov/geospatial/national-transportation-noise-map, for downloads and more information about these datasets. For web services of these data, please navigate to https://geo.dot.gov/server/rest/services/Hosted and search for service names beginning with "Noise." Please contact the NTAD Program Manager at ntad@dot.gov for any questions. Data within the National Transportation Noise Map represent potential noise levels across the nation for an average annual day for the specified year. These data are intended to facilitate the tracking of trends in transportation-related noise by mode collectively over time and should not be used to evaluate noise levels in individual locations and/or at specific times. This dataset is developed using a 24-hr equivalent A-weighted sound level (denoted by LAeq) noise metric. The results represent the approximate average noise energy due to transportation noise sources over a 24-hour period at the receptor locations where noise is computed. Layers include Aviation, Passenger Rail (prototype), and Road Noise for the Lower 48 States as well as Alaska and Hawaii. The full listing can be found below. 2018 National Transportation Noise

      Alaska

             Alaska Aviation Noise
             Alaska Road and Aviation Noise
             Alaska Road Noise

      Lower 48 States (CONUS)

             Lower 48 States (CONUS) Aviation Noise
             Lower 48 States (CONUS) Passenger Rail Noise (prototype)
             Lower 48 States (CONUS) Passenger Rail, Road, and Aviation Noise (prototype)
             Lower 48 States (CONUS) Road and Aviation Noise
             Lower 48 States (CONUS) Road Noise

      Hawaii

             Hawaii Aviation Noise
             Hawaii Road and Aviation Noise
             Hawaii Road Noise

Defra has published strategic noise map data that give a snapshot of the estimated noise from major road and rail sources across England in 2017. The data was developed as part of implementing the Environmental Noise Directive.

This publication explains which noise sources were included in 2017 strategic noise mapping process. It provides summary maps for major road and rail sources and provides links to the detailed Geographic Information Systems (GIS) noise datasets.

This data will help transport authorities to better identify and prioritise relevant local action on noise. It will also be useful for planners, academics and others working to assess noise and its impacts.

Noise mapping Geographic Information Systems (GIS) datasets

Rail noise

• https://environment.data.gov.uk/dataset/5836745c-4e11-4767-94a5-2656f82e01a3">Laeq 16h: indicates the annual average noise levels for the 16-hour period between 0700 – 2300

• https://environment.data.gov.uk/dataset/e8e78e12-9297-450b-b875-e0523cb3c9ea">Lden: indicates a 24 hour annual average noise level with separate weightings for the evening and night periods

• https://environment.data.gov.uk/dataset/f6c0e3b6-3186-4d0a-b0e7-ca32bfb6573f">Lnight: indicates night time annual average noise level results in dB, where night is defined as 2300 - 0700

Road Noise

• https://environment.data.gov.uk/dataset/b9c6bf30-a02d-4378-94a0-2982de1bef86">Laeq 16h: indicates the annual average noise levels for the 16-hour period between 0700 – 2300

• https://environment.data.gov.uk/dataset/fd1c6327-ad77-42ae-a761-7c6a0866523d">Lden: indicates a 24 hour annual average noise level with separate weightings for the evening and night periods.

• https://environment.data.gov.uk/dataset/cc48e728-602a-4e8a-9221-49f661ab58f8">Lnight: indicates night time annual average noise level results in dB, where night is defined as 2300 - 0700

Other

• https://environment.data.gov.uk/dataset/4739c0c3-e800-4cb1-89cd-e71115b191e9">Agglomerations: urban areas Defra is required to undertake strategic noise mapping
• https://environment.data.gov.uk/dataset/55cdd950-fd18-421a-affd-130d659dc6fd">Noise action planning important areas: provide a framework for the local management of the Important Areas.

Noise exposure data

We’ve published data which shows the estimated number of people affected by noise from road traffic, railway and industrial sources.

• https://data.gov.uk/dataset/d461bbc1-eb51-4852-8a9a-45dbf28aa230/noise-exposure-data-round-3">Noise exposure data

1.59 GB; md5sum: f87fb213c0e1c439e1b727fb258ef2cd QUT Research Data Respository Dataset Resource available for download

Data indicating the level of noise according to the strategic noise mapping of road sources within areas with a population of at least 100,000 people (agglomerations) and along major traffic routes. Lden indicates a 24 hour annual average noise level with separate weightings for the evening and night periods. Noise levels are modeled on a 10m grid at a receptor height of 4m above ground, polygons are then produced by merging neighboring cells within the following noise classes: 75.0+ dB, 70.0-74.9 dB, 65.0-69.9 dB, 60.0-64.9 dB, 55.0-59.9 dB, <54.9 dB This data is a product of the strategic noise mapping analysis undertaken in 2017 to meet the requirements of the Environmental Noise Directive (Directive 2002/49/EC) and the Environmental Noise (England) Regulations 2006 (as amended).

Noise Monitoring Data In India (2011 - 2018)
This dataset falls under the category Environmental Data Noise Data.
It contains the following data: The dataset contains noise level readings at monthly level of various stations across seven cities in India.
This dataset was scouted on 2022-02-28 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/rohanrao/noise-monitoring-data-in-india?select=stations.csv

NIGENS (Neural Information Processing group GENeral sounds) is a database provided for sound-related modeling in the field of computational auditory scene analysis, particularly for sound event detection, that has emerged from the Two!Ears project.

It contains 1017 wav files of various lengths (between 1s and 5mins), in total comprising 4h:46m of sound material. Mostly, sounds are provided with 32-bit precision and 44100 Hz sampling rate. The files contain sound events in isolation, i.e. without superposition of ambient or other foreground sources.

Fourteen distinct sound classes are included: alarm, crying baby, crash, barking dog, running engine, burning fire, footsteps, knocking on door, female and male speech, female and male scream, ringing phone, piano. Additionally, there is the general (“anything else”) class. Care has been taken to select sound classes representing different features, like noise-like or pronounced, discrete or continuous.

The general class is a pool of sound events different than the 14 distuingished target sound classes, containing as heterogeneous sounds as possible (303 in total). For example, it includes nature sounds such as wind, rain, or animals, sounds from human-made environments such as honks, doors, or guns, as well as human sounds like coughs. These sounds are intended both as ``disturbance'' sound events (superposing) and as counterexamples to target sound classes.

Wav files are accompanied by annotation (.txt) files that include perceptual on- and offset times of the file's sound events.

You are free to use this database non-commercially under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 license.

If you use this data set, please cite as:

Ivo Trowitzsch, Jalil Taghia, Youssef Kashef, and Klaus Obermayer (2019). The NIGENS general sound events database. Technische Universität Berlin, Tech. Rep. arXiv:1902.08314 [cs.SD]

In [1], we have developed and analyzed a robust binaural sound event detection training scheme using NIGENS. In [2], we have extended it to join sound event detection and localization through spatial segregation.

[1] Trowitzsch, I., Mohr, J., Kashef, Y., Obermayer, K. (2017). Robust detection of environmental sounds in binaural auditory scenes. IEEE/ACM Transactions on Audio, Speech, and Language Processing 25(6).

[2] Trowitzsch, I., Schymura, C., Kolossa, D., Obermayer, K. (2019). Joining Sound Event Detection and Localization Through Spatial Segregation. accepted for publication in IEEE/ACM Transactions on Audio, Speech, and Language Processing. DOI: 10.1109/TASLP.2019.2958408. E-Preprint: arXiv:1904.00055 [cs.SD].

The dataset contains workplace noise measurement results collected during health hazards evaluation surveys from 1996 to 2013 for over 580 area noise level assessments. The collected data about exposure are based on OSHA and NIOSH assessment criteria and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.

The dataset provides noise data to facilitate the tracking of trends in transportation-related noise. This dataset includes results from simplified noise modeling methods and should not be used to evaluate noise levels in individual locations. See the documentation for a full description of methodologies and assumptions: https://doi.org/10.21949/1519111

The 2018 National Transportation Noise Map dataset utilized transportation mode input data from 2018 in a model and is current as of October 2020, published by the Bureau of Transportation Statistics (BTS), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Please see the website https://www.bts.gov/geospatial/national-transportation-noise-map, for downloads and more information about these datasets. For web services of these data, please navigate to https://geo.dot.gov/server/rest/services/Hosted and search for service names beginning with "Noise."

Data within the National Transportation Noise Map represent potential noise levels across the nation for an average annual day for the specified year. These data are intended to facilitate the tracking of trends in transportation-related noise by mode collectively over time and should not be used to evaluate noise levels in individual locations and/or at specific times. This dataset is developed using a 24-hr equivalent A-weighted sound level (denoted by LAeq) noise metric. The results represent the approximate average noise energy due to transportation noise sources over a 24-hour period at the receptor locations where noise is computed. Layers include Aviation, Passenger Rail (prototype), and Road Noise for the Lower 48 States as well as Alaska and Hawaii. The full listing can be found below.

2018 National Transportation Noise

Alaska Alaska Aviation Noise Alaska Road and Aviation Noise Alaska Road Noise Lower 48 States (CONUS) Lower 48 States (CONUS) Aviation Noise Lower 48 States (CONUS) Passenger Rail Noise (prototype) Lower 48 States (CONUS) Passenger Rail, Road, and Aviation Noise (prototype) Lower 48 States (CONUS) Road and Aviation Noise Lower 48 States (CONUS) Road Noise Hawaii Hawaii Aviation Noise Hawaii Road and Aviation Noise Hawaii Road Noise

Dataset

This dataset was created by Tuan Nguyen Van Anh

Contents

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) aircraft noise reports and if available, the correlated flight operation’s details for those reports. B. HOW THE DATASET IS CREATED SFO Noise Office collects this information via the WebTrak, Web, App, Complaint Hotline, emails, letters, and telephone calls to our office. This information is compiled and published in a monthly report which is presented at the SFO Airport Community Roundtable meetings (https://sforoundtable.org/). It serves to help understand community's aircraft noise concerns, to collaborate with all stakeholders in an effort to reduce and manage aircraft noise. C. UPDATE PROCESS Data is available starting in August 2019 and will be updated on a monthly basis. D. HOW TO USE THIS DATASET This data is the data source used to produce the Noise Reports section on page 5 of the monthly Airport Director’s Report. These reports are available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report E. RELATED DATASETS Previously provided data, Aircraft Noise Complaint Data, from January 2005 to July 2019 is available here: https://data.sfgov.org/Transportation/Aircraft-Noise-Complaint-Data/q3xd-hfi8 Please contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.

DEMAND: Diverse Environments Multichannel Acoustic Noise Database

Silencio’s Street Noise-Level Dataset provides unmatched value environmental data industry, delivering highly granular noise data to researchers, developers, and governments. Built from over 35 billion datapoints collected globally via our mobile app and refined through AI-driven interpolation, this dataset offers hyper-local average noise levels (dBA) covering streets, neighborhoods, and venues across more than 180+ countries.

Our data helps assess the environmental quality of any location, supporting residential and commercial property valuations, site selection, and urban development. By integrating real-world noise measurements with AI-powered models, we enable real estate professionals to evaluate how noise exposure impacts property value, livability, and buyer perception — factors often overlooked by traditional market analyses.

Silencio also operates the largest global database of noise complaints, providing additional context for understanding neighborhood soundscapes from both objective measurements and subjective community feedback.

We offer on-demand visual delivery for mapped cities, regions, or even specific streets and districts, allowing clients to access exactly the data they need. Data is available both as historical and up-to-date records, ready to be integrated into valuation models, investment reports, and location intelligence platforms. Delivery options include CSV exports, S3 buckets, PDF, PNG, JPEG, and we are currently developing a full-featured API, with flexibility to adapt to client needs. We are open to discussion for API early access, custom projects, or unique delivery formats.

Fully anonymized and fully GDPR-compliant, Silencio’s data ensures ethical sourcing while providing real estate professionals with actionable insights for smarter, more transparent valuations.