1184 Global import shipment records of Acoustic Insulation Material with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Automotive Acoustic Materials Market size was valued at USD 2.99 Billion in 2024 and is projected to reach USD 4.14 Billion by 2031, growing at a CAGR of 4.60 % during the forecast period 2024-2031.

Global Automotive Acoustic Materials Market Drivers

The market for automotive acoustic materials is influenced by a number of important variables, including as consumer preferences, industry-specific trends, technological developments, and regulatory requirements. Automotive acoustic materials improve comfort and driving experience by lowering noise, vibration, and harshness (NVH) levels in automobiles. The following are some of the main forces influencing the market for vehicle acoustic materials:

Growing Customer Demand for Comfort and Quietness: Automakers are concentrating on lowering interior noise levels in cars as a result of consumers’ growing preference for comfort and a quieter driving experience. By isolating noise sources, absorbing sound, and damping vibrations, automotive acoustic materials are essential to reaching this goal. The market is expanding as a result of the rising demand for acoustic materials that raise cabin comfort and lower NVH levels.

Strict Regulations: The use of automotive acoustic materials is being influenced by government laws and regulations on occupant safety and vehicle noise emissions. Automakers must adhere to specific noise standards, and regulatory agencies globally set limitations on the amount of noise that can be heard inside vehicles. The demand for acoustic solutions in the automotive industry is rising as a result of automakers using advanced acoustic materials in vehicle designs to achieve these regulations.

Progress in Material Science and Engineering: The creation of novel automotive acoustic materials with improved performance properties is the result of technological breakthroughs in material science and engineering. In order to build acoustic solutions that offer excellent noise reduction properties while optimising weight and space utilisation in vehicles, manufacturers are utilising cutting-edge materials like composite materials, lightweight foams, and sound-absorbing textiles. The use of acoustic materials in contemporary car designs is fueled by these developments.

Put an emphasis on lightweighting and fuel efficiency: To meet strict emissions rules and fuel economy criteria, automakers are focusing more on improving fuel efficiency and decreasing vehicle weight in addition to improving acoustic comfort. Because they help reduce weight and noise in cars, lightweight acoustic materials have two advantages. The need for creative lightweight acoustic materials keeps rising as automakers look for lightweight options without sacrificing acoustic performance.

Growing Car Production and Sales: Rising disposable incomes, urbanisation, and the growing demand for passenger cars, especially in emerging economies, are all contributing to the continuous rise of the global automotive sector. Automotive acoustic materials are in higher demand proportionate to increases in vehicle production and sales volumes. To keep up with the increasing demand from the automotive industry, automakers and suppliers are investing in raising manufacturing capacity and creating innovative acoustic solutions.

Growing Adoption of Electric and Hybrid automobiles: The market for automotive acoustic materials is being impacted by the move towards electric and hybrid automobiles. Since the powertrains of electric and hybrid cars are quieter than those of conventional internal combustion engine vehicles, interior noise control is even more important to guaranteeing passenger comfort. The need for specialised acoustic materials created to solve the particular NVH issues related to electric powertrains is increasing as automakers make investments in the development of electric vehicles.

Innovation in Interior Design and Luxurious capabilities: Premiumization is a trend in the car industry, as customers want opulent interiors with cutting-edge comfort and entertainment capabilities. Automotive acoustic materials offer excellent sound quality, noise absorption, and overall cabin comfort, making them essential for designing luxurious interior spaces. Luxury car manufacturers are differentiating their vehicles and satisfying customer demands for upscale driving experiences by adding premium acoustic materials.

7 Active Global Acoustic Insulation Material buyers list and Global Acoustic Insulation Material importers directory compiled from actual Global import shipments of Acoustic Insulation Material.

This dataset provides the data for Reference

Merten Stender, Christian Adams, Mathies Wedler, Antje Grebel, and Nobert Hoffmann:Explainable machine learning determines effects on the sound absorption coefficientmeasured in the impedance tube. J. Acoust. Soc. Am. 149 (3), 2021

It consists of two csv files:

alphas.csv: absorption coefficients vs. frequencies (comma-separated), 3079 rows accordingto 3079 measurements

targets.csv: one-hot encoded, i.e., binary, vectors of the parameter combinations), 3079rows according to 3079 measurements. The Read_Me.pdf gives further information on the one-hot encoded vectors.

The frequencies range from 269 Hz to 2191 Hz with resolution of 1 Hz. Note that these limits are notstrictly equal to the frequency limits of the impedance tube, see [1].

The data and code are licensed under Apache License, Version 2.0https://opensource.org/licenses/Apache-2.0

Any reuse of the data must properly cite [1] and its authors.

Contact:Univ.-Prof. Dr. Christian AdamsGraz University of TechnologyInffeldgasse 16c8010 Graz, Austriachristian.adams@tugraz.at

According to Cognitive Market Research, the global acoustic insulation market size will be USD 13.9 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 3.9% from 2024 to 2031. Market Dynamics of Acoustic Insulation Market

Key Drivers for Acoustic Insulation Market

Growing health concern linked to noise pollution — People have been impacted by noise pollution since the late 18th century when the industry first began. Long-term exposure to noise has been shown to have a number of detrimental health impacts, including impaired cognitive function in children, sleep disturbance, hearing loss, irritation, stress risks, and harmful effects on the cardiovascular and metabolic systems. About 20% of Europeans are exposed to long-term, dangerous noise levels, according to the European Environment Agency (EEA). Furthermore, according to recent EEA data, environmental noise causes 12,000 premature deaths and 48,000 new cases of ischemic heart disease annually. Additionally, 6.5 million individuals experience severe sleep disturbances, and roughly 22 million people experience chronic high irritation. In addition, it is projected that 12,500 students will have reading difficulties in 2020 as a result of airplane noise. A European Union (EU) document states that over 40% of people living in EU member states are exposed to road traffic noise at levels higher than 55 dB(A); 20% are exposed to levels higher than 65 dB(A) during the day; and over 30% are exposed to levels higher than 55 dB(A) at night. Therefore, governments around the world are enforcing laws that restrict noise in order to address this problem. Spending more on infrastructure in developing nations

Key Restraints for Acoustic Emission Testing Market

Fluctuating costs for raw materials as a result of changes in the price of crude oil Low knowledge and expensive initial outlay for acoustic insulation in emerging nations Introduction of the Acoustic Insulation Market

Soundproofing, also known as acoustic insulation, is the ability of a substance to either absorb or reflect sound waves in order to improve acoustic comfort. Among the materials that are frequently used for acoustic insulation are bitumen sheets, mineral wool, fiber belts, fiberglass, blown-in cellulose, spray foam, and foam board. These materials reduce sound pollution by having better vibration-damping and sound-absorbing properties. They can lessen impact noises, flanking sounds like those coming from hollow core doors and ducts, and airborne sounds like car horns, radios, and loudspeakers. Because of this, acoustic insulation materials are extensively utilized in a variety of sectors, including transportation, energy, oil & gas, and construction.

16 Active Global Acoustic Insulation Material buyers list and Global Acoustic Insulation Material importers directory compiled from actual Global import shipments of Acoustic Insulation Material.

Appendix A: Material and acoustic treatment sound absorption and cost data

The IDMT-ISA-PUCKS dataset (IIPD) was designed to simulate the challenging acoustic analysis conditions consistent with industrial manufacturing settings. The dataset contains audio recordings of multiple games of air-hockey played with pucks of different plastic materials. Data collection was performed by equipping the air hockey table with two sE8 microphones, each recording one side of the table, as seen in the image above, while a game is played. Additionally, there are recordings where no game was being played and only background noise was recorded.

We recorded the games played with different pucks at three different noise levels: Level 1 at room volume (vol_000), Level 2 with some background noise (vol_050 = 70 CBR) and Level 3 at loud background noise (vol_100 = 80 CBR). The background noise was played over four speakers in equal distances around the table and contains human voices.

The following materials were used for the four pucks:

• Puck_A is the original factory puck (material unknown)
• Puck_E from the 3D printer (material: ABS, print process: FDM)
• Puck_G from the 3D printer (material: PA2200, print process: SLS)
• Puck_I from the 3D printer (material: PA12, print process: MJF)

For each noise level and puck material, five three-minute games were played with different pucks of the specified material. Further, each game was played with different sets of players. The recordings were made via two sE8 microphones placed in the middle of the air-hockey table (about 10 cm above the surface).

Dataset total duration: 260 minutes (1 min per file)

• # Files for puck_A: 45
• # Files for puck_E: 45
• # Files for puck_G: 45
• # Files for puck_I: 45
• # Files for no_puck: 45
• # Total WAV Files: 260
• Sampling rate: 44.1KHz
• Resolution: 32-bit
• Stereo audio

The global window acoustic vent market, currently valued at $60.2 million in 2025, is projected to experience steady growth, driven by increasing demand for noise reduction in residential and commercial buildings. A Compound Annual Growth Rate (CAGR) of 4% is anticipated from 2025 to 2033, reflecting a growing awareness of noise pollution's impact on health and well-being, coupled with stricter building codes in numerous regions. Key drivers include rising urbanization, leading to increased noise levels in densely populated areas, and a growing preference for energy-efficient windows that incorporate sound-dampening features. Market segmentation reveals a strong demand across various applications, including commercial buildings (offices, hotels) and family residences, with varied sizes (1700mm², 2700mm², 4400mm², 5000mm²) catering to different window dimensions and noise reduction needs. Competition is robust, with key players such as Duco, DB Acoustic, and Siegenia offering diverse product lines and technological advancements to maintain a competitive edge. The market’s expansion is likely to be geographically diverse, with North America and Europe expected to maintain significant market share due to existing infrastructure and stringent environmental regulations. The market's growth trajectory will be influenced by several factors. Technological advancements, including the development of more efficient acoustic materials and improved vent designs, will contribute to increased market penetration. Furthermore, rising construction activity globally, particularly in emerging economies, will provide substantial opportunities. However, economic fluctuations and the potential for material cost increases could pose challenges. The ongoing emphasis on sustainable building practices and energy efficiency will favor the adoption of acoustic vents that enhance energy savings and environmental performance. The market is expected to see further segmentation based on material types (e.g., aluminum, PVC), further enhancing product diversification and consumer choice. This robust competitive landscape will incentivize continuous innovation and value-added offerings, sustaining the market's steady growth throughout the forecast period.

The proposed innovation is to use combustion synthesis techniques to manufacture ceramic-based acoustic liners capable of withstanding temperatures up to 2500?C. Combustion synthesis or self-propagating high temperature synthesis (SHS) is a novel technique used by Guigne Space Systems Inc. to produce many advanced high-temperature materials and composites. The materials have a ceramic matrix (alumina Al2O3, MgO, Al2O3-MgO, TiC-Al2O3, or Al2O3-TiB2) and exhibit high porosity. These materials can also be fabricated with a functional gradient, i.e., with a change in chemistry and/or porosity within the same sample. When compared to traditional manufacturing techniques for high-temperature materials, combustion synthesis has the advantages of energy and time saving methods, high purity final product, simplicity of process and low cost. The target application for the porous ceramics is as high temperature acoustic liners for noise reduction in rocket and jet engines. The proposed work is Phase I of the project.

The global window acoustic vent market, currently valued at $60.2 million in 2025, is projected to experience steady growth, driven by increasing concerns about noise pollution in residential and commercial buildings. A compound annual growth rate (CAGR) of 4% is anticipated through 2033, indicating a substantial market expansion. Key drivers include rising urbanization leading to denser populations and increased noise levels, stricter building codes mandating noise reduction measures, and growing awareness of the health impacts of prolonged noise exposure. The market is segmented by application (commercial buildings and family residences), with commercial buildings currently holding a larger share due to higher noise levels in urban environments and the need for improved acoustic comfort in office spaces. Further segmentation by equivalent area (1700mm², 2700mm², 4400mm², 5000mm², and others) reflects the diverse needs of different building types and sizes. Major players like Duco, DB Acoustic, and Siegenia are actively shaping the market through product innovation and strategic partnerships, focusing on energy efficiency and design aesthetics alongside acoustic performance. The North American and European markets currently dominate, but significant growth potential exists in Asia-Pacific regions due to rapid urbanization and infrastructure development. The market's growth trajectory is expected to be influenced by several factors. While increased construction activity fuels demand, economic fluctuations and material costs can present challenges. Technological advancements leading to more effective and aesthetically pleasing acoustic vent solutions will be critical in maintaining a strong growth momentum. Furthermore, the integration of smart home technologies and the increasing demand for sustainable building materials present significant opportunities for market expansion. Competition among established players and emerging companies is likely to intensify, focusing on differentiation through enhanced product features, improved energy efficiency, and customized solutions tailored to specific market segments. The market’s future success hinges on continued innovation, strategic partnerships, and effective marketing strategies to reach a wider audience conscious of the importance of noise reduction in their living and working environments.

The Passenger Car Acoustic Material market plays a crucial role in enhancing the comfort and quality of modern automotive experiences by effectively reducing noise and vibrations inside vehicles. As consumers increasingly prioritize cabin comfort, the demand for specialized acoustic materials has surged. These mater

The scoring of the articles used in this study.

FSDKaggle2019 is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019.

Citation

If you use the FSDKaggle2019 dataset or part of it, please cite our DCASE 2019 paper:

Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Serra. "Audio tagging with noisy labels and minimal supervision". Proceedings of the DCASE 2019 Workshop, NYC, US (2019)

You can also consider citing our ISMIR 2017 paper, which describes how we gathered the manual annotations included in FSDKaggle2019.

Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, "Freesound Datasets: A Platform for the Creation of Open Audio Datasets", In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017

Data curators

Eduardo Fonseca, Manoj Plakal, Xavier Favory, Jordi Pons

Contact

You are welcome to contact Eduardo Fonseca should you have any questions at eduardo.fonseca@upf.edu.

ABOUT FSDKaggle2019

Freesound Dataset Kaggle 2019 (or FSDKaggle2019 for short) is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology [1]. FSDKaggle2019 has been used for the Task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2019. Please visit the DCASE2019 Challenge Task 2 website for more information. This Task was hosted on the Kaggle platform as a competition titled Freesound Audio Tagging 2019. It was organized by researchers from the Music Technology Group (MTG) of Universitat Pompeu Fabra (UPF), and from Sound Understanding team at Google AI Perception. The competition intended to provide insight towards the development of broadly-applicable sound event classifiers able to cope with label noise and minimal supervision conditions.

FSDKaggle2019 employs audio clips from the following sources:

Freesound Dataset (FSD): a dataset being collected at the MTG-UPF based on Freesound content organized with the AudioSet Ontology

The soundtracks of a pool of Flickr videos taken from the Yahoo Flickr Creative Commons 100M dataset (YFCC)

The audio data is labeled using a vocabulary of 80 labels from Google’s AudioSet Ontology [1], covering diverse topics: Guitar and other Musical Instruments, Percussion, Water, Digestive, Respiratory sounds, Human voice, Human locomotion, Hands, Human group actions, Insect, Domestic animals, Glass, Liquid, Motor vehicle (road), Mechanisms, Doors, and a variety of Domestic sounds. The full list of categories can be inspected in vocabulary.csv (see Files & Download below). The goal of the task was to build a multi-label audio tagging system that can predict appropriate label(s) for each audio clip in a test set.

What follows is a summary of some of the most relevant characteristics of FSDKaggle2019. Nevertheless, it is highly recommended to read our DCASE 2019 paper for a more in-depth description of the dataset and how it was built.

Ground Truth Labels

The ground truth labels are provided at the clip-level, and express the presence of a sound category in the audio clip, hence can be considered weak labels or tags. Audio clips have variable lengths (roughly from 0.3 to 30s).

The audio content from FSD has been manually labeled by humans following a data labeling process using the Freesound Annotator platform. Most labels have inter-annotator agreement but not all of them. More details about the data labeling process and the Freesound Annotator can be found in [2].

The YFCC soundtracks were labeled using automated heuristics applied to the audio content and metadata of the original Flickr clips. Hence, a substantial amount of label noise can be expected. The label noise can vary widely in amount and type depending on the category, including in- and out-of-vocabulary noises. More information about some of the types of label noise that can be encountered is available in [3].

Specifically, FSDKaggle2019 features three types of label quality, one for each set in the dataset:

curated train set: correct (but potentially incomplete) labels

noisy train set: noisy labels

test set: correct and complete labels

Further details can be found below in the sections for each set.

Format

All audio clips are provided as uncompressed PCM 16 bit, 44.1 kHz, mono audio files.

DATA SPLIT

FSDKaggle2019 consists of two train sets and one test set. The idea is to limit the supervision provided for training (i.e., the manually-labeled, hence reliable, data), thus promoting approaches to deal with label noise.

Curated train set

The curated train set consists of manually-labeled data from FSD.

Number of clips/class: 75 except in a few cases (where there are less)

Total number of clips: 4970

Avg number of labels/clip: 1.2

Total duration: 10.5 hours

The duration of the audio clips ranges from 0.3 to 30s due to the diversity of the sound categories and the preferences of Freesound users when recording/uploading sounds. Labels are correct but potentially incomplete. It can happen that a few of these audio clips present additional acoustic material beyond the provided ground truth label(s).

Noisy train set

The noisy train set is a larger set of noisy web audio data from Flickr videos taken from the YFCC dataset [5].

Number of clips/class: 300

Total number of clips: 19,815

Avg number of labels/clip: 1.2

Total duration: ~80 hours

The duration of the audio clips ranges from 1s to 15s, with the vast majority lasting 15s. Labels are automatically generated and purposefully noisy. No human validation is involved. The label noise can vary widely in amount and type depending on the category, including in- and out-of-vocabulary noises.

Considering the numbers above, the per-class data distribution available for training is, for most of the classes, 300 clips from the noisy train set and 75 clips from the curated train set. This means 80% noisy / 20% curated at the clip level, while at the duration level the proportion is more extreme considering the variable-length clips.

Test set

The test set is used for system evaluation and consists of manually-labeled data from FSD.

Number of clips/class: between 50 and 150

Total number of clips: 4481

Avg number of labels/clip: 1.4

Total duration: 12.9 hours

The acoustic material present in the test set clips is labeled exhaustively using the aforementioned vocabulary of 80 classes. Most labels have inter-annotator agreement but not all of them. Except human error, the label(s) are correct and complete considering the target vocabulary; nonetheless, a few clips could still present additional (unlabeled) acoustic content out of the vocabulary.

During the DCASE2019 Challenge Task 2, the test set was split into two subsets, for the public and private leaderboards, and only the data corresponding to the public leaderboard was provided. In this current package you will find the full test set with all the test labels. To allow comparison with previous work, the file test_post_competition.csv includes a flag to determine the corresponding leaderboard (public or private) for each test clip (see more info in Files & Download below).

Acoustic mismatch

As mentioned before, FSDKaggle2019 uses audio clips from two sources:

FSD: curated train set and test set, and

YFCC: noisy train set.

While the sources of audio (Freesound and Flickr) are collaboratively contributed and pretty diverse themselves, a certain acoustic mismatch can be expected between FSD and YFCC. We conjecture this mismatch comes from a variety of reasons. For example, through acoustic inspection of a small sample of both data sources, we find a higher percentage of high quality recordings in FSD. In addition, audio clips in Freesound are typically recorded with the purpose of capturing audio, which is not necessarily the case in YFCC.

This mismatch can have an impact in the evaluation, considering that most of the train data come from YFCC, while all test data are drawn from FSD. This constraint (i.e., noisy training data coming from a different web audio source than the test set) is sometimes a real-world condition.

LICENSE

All clips in FSDKaggle2019 are released under Creative Commons (CC) licenses. For attribution purposes and to facilitate attribution of these files to third parties, we include a mapping from the audio clips to their corresponding licenses.

Curated train set and test set. All clips in Freesound are released under different modalities of Creative Commons (CC) licenses, and each audio clip has its own license as defined by the audio clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. The licenses are specified in the files train_curated_post_competition.csv and test_post_competition.csv. These licenses can be CC0, CC-BY, CC-BY-NC and CC Sampling+.

Noisy train set. Similarly, the licenses of the soundtracks from Flickr used in FSDKaggle2019 are specified in the file train_noisy_post_competition.csv. These licenses can be CC-BY and CC BY-SA.

In addition, FSDKaggle2019 as a whole is the result of a curation process and it has an additional license. FSDKaggle2019 is released under CC-BY. This license is specified in the LICENSE-DATASET file downloaded with the FSDKaggle2019.doc zip file.

FILES & DOWNLOAD

FSDKaggle2019 can be downloaded as a series of zip files with the following directory structure:

root │
└───FSDKaggle2019.audio_train_curated/ Audio clips in the curated train set │ └───FSDKaggle2019.audio_train_noisy/ Audio clips in the noisy

The data set contains the results of room acoustic measurements and additional information from 57 rooms in 19 different public preschools in the Gothenburg area (Sweden). The measurements were conducted during 2019-2020. Children at preschool are divided into units commonly, but not always, based on age. The presented data covers in total 31 different units aimed at older children. Up to three rooms per unit were measured, focusing on the main play rooms and the meal room. In cases where these rooms were the same, only one room per unit was measured.

The preschools are divided into three groups, strata, based on the year when they were built: 1980-1994, 1995-2006, and 2007-2018.

To reach an even distribution within each strata with respect to socioeconomic factors, we used an existing preschool-specific index acquired from the central preschool administration Jorsäter, M., Resursfördelningsmodell Göteborg 2019. 2019, Statistics Sweden (SCB). The index is based on a model from Statistics Sweden (SCB) where school performance after elementary school is linked with a number of explanatory variables related to the socioeconomic background of the individual child and her/his parents. When these factors are known for the children at a specific preschool, an averaged index is calculated, centered around 100. Preschools with an index over 100 have a larger share of children with a risk of not qualifying for high school (gymnasium). Preschools with an index less than 100 have a smaller share of children with a risk of not qualifying for high school. The purpose of the index is to prioritize economic support to school units with the highest need and increase equity.

Room acoustic parameters and unoccupied noise levels were measured in each room using a laptop and an external 8-channel sound card (HEAD acoustics SQuadriga II). An omnidirectional sound source with a built-in generation of pink noise (50-20000 Hz) was used to excite the room. A modification was made to the device in order to extract the electronic signal from the loudspeaker.

Unoccupied noise levels were measured in accordance with ISO 16032:2004. In some rooms the contribution from the ventilation could be estimated by conducting the measurements with the ventilation unit turned on and off separately.

Room acoustic measurements were conducted following the precision method in ISO 3382-2:2008. Three microphones were used simultaneously with predetermined height intervals (1±0.2, 1.4±0.2, 1.6±0.2). The standard’s recommendation to use natural source positions was implemented by placing the loudspeaker where we estimated that a child would have its head during sitting and standing activities in the room. In addition, a corner position was always used. Impulse responses were calculated by the software ArtemiS SUITE using the loudspeaker's extracted electronic signal as the reference.

Impulse responses and unoccupied sound pressure level spectra were exported to Matlab. Octave band room acoustic parameters were calculated for 125 – 8000 Hz from the impulse responses with the ITA-toolbox 8.6 [Berzborn, M., et al., The ITA-Toolbox: An Open Source MATLAB Toolbox for Acoustic Measurements and Signal Processing, in DAGA 2017. 2017: Kiel.]. The analysis was done for reverberation time T20 and EDT (Early decay time), and Speech Clarity, C50. An addition to the code was made to evaluate the Clarity index with a shorter time, C35, which uses 35 ms instead of the default 50 ms as suggested by Whitlock and Dodd [Whitlock, J.A.T. and G. Dodd, Speech Intelligibility in Classrooms: Specific Acoustical Needs for Primary School Children. Building Acoustics, 2008. 15(1): p. 35-47.]

Sound strength, G (dB), was calculated from the sound power of the loudspeaker LW (measured according to the ISO 3741 standard) and the resulting sound pressure level Lp in a measurement point: G=Lp - LW + 31 [SS-EN ISO 3382-1:2009].

Additional information collected during the measurements: Dimensions and shape of the room The floor and wall type of construction material is classified into heavy or lightweight A subjective evaluation of the degree of furnishing and categorized as sparse, normal or dense. Material type of the acoustic treatment in the ceiling: porous type (typical white mineral wool) or other various types, e.g. perforated gypsum boards Sound absorption on the walls* and their approximate total area

*The concept wall absorption is interpreted as a fabric or porous material mounted on, or in the vicinity of a wall and which was subjectively judged as acoustically absorbing.

The data set consists of seven spreadsheet files where each row contains data from one preschool room. Each preschool room is identified with 4 numbers: ROOM_ID1: Represents building year interval: “1”=1980-1994, “2”=1995-2006, “3”=2007-2018 ROOM_ID2: Represents the number of the preschool within each building year interval ROOM_ID3: Represents the unit number within each preschool ROOM_ID4: Represents the room number within each unit

Missing data is indicated with an empty cell.

The data is also available as semicolon-separated .csv files.

XLSX-file “INFO” SES = Socioeconomic index of preschool Volume = Volume of room in (m3) FloorA = Floor area (m2) Height_min / Height_max = height to inner ceiling (m) FurnDeg = Subjective furnishing degree, 1 = spares, 2 = normal, 3 = dense FloorConst = Floor construction, 1 = lighweight, 2 = heavy (concrete) WallConst = Wall construction, 1 = lightweight, 2 = heavy, 3= mixed CeilingAbs = Acoustic ceiling, 1 = porous (mineral wool), 2 = other (commonly perforated boards) wallAbs = Amount of wall absorbers (m2)

Measured room acoustic parameters are presented as room-averaged for the three different microphone heights separately: low = 1±0.2, mid = 1.4±0.2 and high = 1.6±0.2 m and for the octave bands from 125 Hz to 8000 Hz:

XLSX-file “T20” Reverberation time T20 (s)

XLSX-file “EDT” Early decay time (s)

XLSX-file “G” Sound strength (dB)

XLSX-file “C50” Speech Clarity (dB)

XLSX-file “C35” Speech Clarity with integration time 35 ms (dB)

Measured unoccupied noise levels are presented as Leq (equivalent) levels in 1/3 octave bands from 25 Hz to 10000 Hz in the spreadsheet file “BKG.xlsx”. For some rooms there is also an estimated contribution from the ventilation system to the background noise equivalent level.

XLSX-file “BKG” Equivalent level (dB)

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Abstract:

The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.

Content:

• 01 StudyDescription.pdf
  • Description of the field study.
  • Information about the methods and materials used.
• 02 Dataset.csv
  • The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.
• 03 VariableDescriptions_EnglishPersonQuestionnaire.pdf
  • Descriptions of all variables, their measurement scale, scale ranges and levels.
  • Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.
  • English translations of questions asked in the person questionnaire.
• 04 ESM-Questionnaire.pdf
  • Screenshots of the original Experience Sampling Method questionnaire with English translations.
• 05 PersonQuestionnaire_OriginalGermanVersion.pdf
  • Original version of the person questionnaire in German language.
• 06 HelpTexts.pdf
  • Descriptions of the study task.
  • Explanations of the scales used in the questionnaire.
  • Explanations of the sound categories and the soundscape composition.
  • Explanation of the operation of the recording device.
• AcousticFeatures_README.md
  • Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.
  • Analyis settings used in Artemis Suite to generate the acoustic features.
• AcousticFeatures_SingleValues.csv
  • All acoustic features, aggregated to single values per feature, recording, and channel.
• AcousticFeatures_Spectra.csv
  • Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.
• AcousticFeatures_Spectrograms.zip
  • 13188 .csv files with un-weighted spetrograms of each channel of each recording.
• AcousticFeatures_TimeSeries.zip
  • A .csv file containing LAeq and LZeq time series of each channel of each recording.

Publications refering to this dataset:

Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."

Funding:

This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6.

License:

CC 4.0 BY, https://creativecommons.org/licenses/by/4.0/legalcode

Version history:

Details can be found in the Changelog.md file.

• V.01.0. March 7, 2023: Initial publication. https://doi.org/10.5281/zenodo.7193938
• V.01.1. April 25, 2023. https://doi.org/10.5281/zenodo.7858848

Here are included figures and other relevant data from the paper "Electro-Acoustic Nonlinear Signal Mixing" to be published in proceedings to The Device Research Conference.Introduction: Here we develop an approach to measuring nonlinear signals generated by piezoelectric materials. While linear measurements of piezoelectric materials are quite common, field- and frequency-dependent measurements of the third order piezoelectric tensor of piezoelectric materials remain rare [1, 2]. Third order piezoelectric properties are critical for next-generation design of electric-acoustic devices such as surface acoustic wave and bulk acoustic wave devices [3]. Our approach uses an electrical interferometer which cancels electrical noise and increases the dynamic range of our electrical measurements.

A series of linear regression models were developed and calibrated for two Lower Minnesota River sites. The linear regression models were either calibrated using acoustic or streamflow data to estimate suspended-sediment or sand concentration data. Data were collected during calendar years 2012 through 2019. The estimates of suspended-sediment and concentrations from the linear regression were used to calculate loads. The calibrated models were used to improve understanding of sediment and sand transport processes and increase accuracy of estimating sediment and sand concentrations and loads for the Lower Minnesota River, as part of the associated report, U.S. Geological Survey Open File Report 2021–1005 (https://doi.org/10.3133/ofr20211005).

Acoustic emission data generated during mode I quasi-static and fatigue crack growth in an aluminium-2024-T3/FM94 epoxy/aluminium-2024-T3 adhesive bond.

The objective was to investigate when and how crack growth occurs during a single fatigue load cycle. Data includes not only the acoustic emission data, but also applied force and displacement, and the crack length.