Find research datasets worth reusing

opencc-by-4.0Nov 2019View details →

ESMA-3D Immersive Soundscape Recordings

ESMA3D (Equal Segment Microphone Array 3D) is a 3D mic array technique for 360° recording developed by Hyunkook Lee of the APL. It consists of 8 microphones arranged in a horizontally spaced, vertically coincident fashion. This provides a very good spaciousness and stable/accurate horizontal and vertical imaging as well as a realistic impression of being there and strong immersion. The main layer (mainly responsible for capture sound sources) is a 50cm x 50cm square of four cardioid microphones spaced with a 90° subtended angle between each pair of mics. The upper layer uses 4 supercardioid mics facing upwards for capturing ambience and elevated sources like birds. The lower and upper layers are coincident (0 spacing, but with a subtended angle of 90 to 120° to have enough level difference between the layers for stable vertical localisation). This design is based on our previous findings showing that vertical mic spacing has little or no contribution to spatial impression in 3D recording. The Schoeps ORTF3D is a brother/sister mic array sharing the same concept - horizontal spacing/vertical coincidence. The reproduction of ESMA3D requires the 8-channel Cube speaker array or 9.1 Dolby Atmos or Auro-3D setup (without the centre and sub), or it can be also binaurally or/and Ambisonically rendered, which still preserves a good quality if used with a good quality decoder, e.g. IEM plugin suite or Aalto SPARTA suite. More details about the array design and listening test results can be found in our AES papers below. <a href="http://www.aes.org/e-lib/browse.cfm?elib=19883&fbclid=IwAR26HtDS31P_QHphsMBDFnqDnbJ51x0rFLX2p5syM1hbvVibXZYOdncaMhQ">http://www.aes.org/e-lib/browse.cfm?elib=19883</a> <a href="https://l.facebook.com/l.php?u=http%3A%2F%2Fwww.aes.org%2Fe-lib%2Fbrowse.cfm%3Felib%3D19401%26fbclid%3DIwAR3jTsNIKKNzQcOueQxKz9UN6nWMUU2EO-A2JSZj4SPKnYBdc9zKSAe_ccU&h=AT0Cbs9tBV7yK9oP1HZSpvyZg_6fuMIDFzQ28eMGdyk-ZWvvLFNFbSIVhs2DACkEdpkIKViisoc0sTfdh2rvOqMG00DaOP4oUOtI5k0gXwleexBnieOE5kqT6es9_4z4FFtXtUFUNMXQVdr2HpsOKCc6S0H2x0KxwTh-s6mrlS14X52JaMj7WkFY1zwyCgpT40fUVueMSJ4mB9kMPjlj-879w3EVKqvsB-Cl8OMT9ct2_k_5A0pbHd77swb6JDSNskcXFEKdyDQ9pfEbcDtFx72CY3aTsj_yv6vYxcSm5BPxObP8EYpfcAyoFcln1O0WnYUYX6uzMRTGd3OU-_YQvWHuDQqlORQIWpAF4BB9QnX7rxfR3Rm_kIfO1QIgjqgcPDJo2nKCjd8gP2KG7Ac4ufXHC8a9BcC1S7WrV91f_pIHWzqj11OB8pbeOeGjuY5Y6jfRY-LAyW3eFGrUMgxd8UUbnFBwDpSIpZRsx9YyKTpNs983FXWsqr2XsJ1KavjFc-JQ05k8MXGyH5NLuihYqJxth1GQw_W9xcdtWBFmJtf8uQpnpF-8wbMozclJuqQoA-rxPCONOZvwHQUsAgJowyOoDUEDpUVe0Sjkww14KneRo9NVu2CkYTYwVgZUTplRBRi2wySV">http://www.aes.org/e-lib/browse.cfm?elib=19401</a> <a href="https://l.facebook.com/l.php?u=http%3A%2F%2Fwww.aes.org%2Fe-lib%2Fbrowse.cfm%3Felib%3D17560%26fbclid%3DIwAR2GrKGsTPT2gJsBu7vkWSnLqh5mq0cU1lRXeE5aCsBpb71A25mV6JIP8Ew&h=AT1YZ64xnlawwJUc7Mcmu00BnfpfaBvjr6HrvNhFQgDBHyq5xszwHe5_VhQK3i_EXjF4B4kgukWHxlCRNjL3VrQH0ry9XMPLmaGiqByTAfsM9GpGgGHvyVnSXVr3-zfHUXZe-ZM6Hb0kHV8y2ioAXaYKiITNCi_XySMmK7qS4FCoAKlHlSMeGzrsPiRoIz4NngSiNkBx0cQCZViHzhwfGB9RNnZYaXQ3vRr2t5biOljgRrKFLPXrgp6CDLvxxtUNerovi7aK0FEHPFow5GUYCPtVhA5eUK0pVES5iy7H34pBv0GvHHvmvR9VSfaS1F1j8VaXqT_9DP5NyiWo8Ap4Lpj6cZqIi0YLH1UP7nAy6LNoW11bxH1fbuugC__jxDG-j6Un_dr_d_m2CY35avEaili6yjNTbgm9u5Wqi1JSXX5fdskdopCAIpLwrgC0pPcXYktG6Q1Ayeb5VX0qlOYtRwbGyuucRrIvJMLB9aH2iHSTHRbCS7fcV0WemP6UQCzHzx6tJ03v9mp42QBbnZ_J03Sr7heeoHEvO7stMyirKMb5e5k5q1RwEevqN_-h75vRm5SzRuhytSINldyDABM6r9vfizp252hQ4fDKcM6dyVT6Jh3gD4F7DqTZ7E2-gsUNaj_hFklV">http://www.aes.org/e-lib/browse.cfm?elib=17560</a> <a href="https://l.facebook.com/l.php?u=http%3A%2F%2Fwww.aes.org%2Fe-lib%2Fbrowse.cfm%3Felib%3D20392%26fbclid%3DIwAR1QEGE8Qq7okwveKMDBNCKVy26ywNJGsbZSVArcg1Bve8nzgB8QH0dYsNE&h=AT3gags5AuxycQ_h8BEzuFaae9wnEkp1r8nXRgdnzVSQlRd0uvcVhqtNcg_J-Swwag5EroiqIEDp-tQgWcRmyTBkAvPOKJ-a9V78ao1ebKW9tUKdp5MGe1y9gK2scHHDptZvPtbxBqS_3vsyhX-9JrriqHyCTzYhgeqp3HfQeAIKNe80AYnXLi1g0Q9TGkyeeChY5ZApLuRWYAwtypWgVMMB5TE7j9RKY1V0Pq7i1-aOV98LwDfFcr3W4bm2GPy8NXNTD-DSv8b61Ez0KCwGRTwMDzwlDE9gSUL5o9CqedzXS4dySoFP4qXrFei3vB33zlrI5uYGzro6ncISIYqnk1ugzHpy4lza-vcHETsa_RNNDrYP_2MenN7QALbaZFhy3ORzGZC0Du98NZ5ycvmOgmv4ChnHhDUzDOnT-B0KfKEn9xn3cOwE_eC28OxxRd9z38kPDyFlZRDc1Asu-9ERNIeYt1npeIulLsr-V8QZApe6BYLHROhYvSjmdiUqVP9aeyem4Mp7XOdo5zjZvfmcpXY0n5y1wsdnWy9N-l0WcSEJkpvEpgApdOnACwNhrv21eIciLwF1xgbb5xTAgeSmGa14mNNxGFwV2PDb6ouF30QtrIrIl-Qv8da92EARBI100LkZahfLGFTXPXo">http://www.aes.org/e-lib/browse.cfm?elib=20392</a>

opencc-by-4.0Aug 2024View details →

Soundscape records (.wav files) used for: Species Assembly of Highland Anuran Communities in Equatorial Africa (Virunga Massif): Soundscape, Acoustic Niches, and Partitioning

The data set comprises sample recordings used for a paper published in "Animals" . Title: "Species assembly of highland anuran communities in equatorial Africa (Virunga Massif): soundscape, acoustic niches and partitioning", authors: Ulrich Sinsch1*, Deogratias Tuyisingize2, J. Maximilian Dehling1 and Yntze van der Hoek2; 1 Institute of Integrated Sciences, Department of Biology, University of Koblenz, D-56070 Koblenz, Germany; <a href="mailto:sinsch@uni-koblenz.de">sinsch@uni-koblenz.de</a>, <a href="mailto:dehling@uni-koblenz.de">dehling@uni-koblenz.de; </a>2 Dian Fossey Gorilla Fund, Ellen DeGeneres Campus, Kinigi, Rwanda; <a href="mailto:dtuyisingize@gorillafund.org">dtuyisingize@gorillafund.org</a>, <a href="mailto:yvanderhoek@gorillafund.org">yvanderhoek@gorillafund.org</a> . Citation: Animals 2024, 14, 2360. https://doi.org/10.3390/ani14162360  https://www.mdpi.com/journal/animals   Descriptor of each file is the heading. Example: Ngezi 20191217_190000 castaneus glandicolor  karissimbensis kivuensis Ngezi = Locality in VNP; 20191217_190000 = record date December 17, 2019, at 19.00 h = 7 pm castaneus glandicolor  karissimbensis kivuensis = Anuran species recorded Hyperolius castaneus, Hyperolius glandicolor, Leptopelis karissimbensis and Leptopelis kivuensis. Further details are given in the text of the paper

opencc-by-4.0Sep 2024View details →

Data for The Design and Formalization of an Embodied Soundscape Sonification Framework

This repository contains evaluation data ane experimental stimuli for the paper :The Design and Formalization of an Embodied Soundscape Sonification Framework.

openother-openAug 2021View details →

A suburban soundscape reveals altered acoustic dynamics during COVID-19 lockdown

Abstract The 2020 COVID-19 pandemic and resulting national and international movement restrictions provide a unique opportunity to investigate the consequences of changing anthropogenic noise regimes on animal communities and soundscapes. Here I use this lockdown period as a natural experiment to investigate changes to soundscape intensity, structure, and dynamics during restricted human activity (lockdown) in suburban Nottingham, UK. Using 11 common acoustic indices, I tested for differences in the richness and evenness of the soundscape during COVID-19 lockdown, and I measured changes in soundscape dynamics by comparing the temporal variability of acoustic indices during versus after lockdown. Regardless of how the soundscape was summarised, there were significant differences in the intensity, evenness, and temporal variability of the soundscape during COVID-19 lockdown, principally driven by changes to anthropogenic noise. I recorded a shift away from a dominance of anthropophony towards more intense biological sounds during lockdown, and the lockdown soundscape was generally more even, particularly because of changes to the magnitude of the diurnal cycle. These preliminary results from a mass human confinement experiment provide an early glimpse into how suburban soundscapes are impacted by noise pollution. In time, globally distributed longer-term monitoring efforts will reveal the generality of these findings, facilitating a mechanistic understanding of the impacts of anthropogenic noise on the world’s natural and human-dominated soundscapes. Methods The dataset contains standardised acoustic index values for 11 commonly used acoustic indices, based on AudioMoth recordings taken during two periods around the COVID-19 lockdown (May 2020) and after restrictions had been lifted (Oct 2020) in suburban Nottingham, UK. I analysed the difference in acoustic index values during versus after the lockdown and compared their temporal variability using standardised effect sizes for the difference between these two time periods. I did this on the whole dataset and on several hourly subsets of the dataset (see the manuscript for further details).  Usage notes See readme file for further details and main manuscript for descriptions of data.

opencc-by-4.0Oct 2021View details →

Synthetic noisy urban soundscapes: a dataset of synthetic soundscapes with real urban backgrounds

Publication   If you use this data in your work, please cite the following paper, which introduced this dataset:   [1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP). [<a href="https://arxiv.org/pdf/1911.02182">pdf</a>] [2] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021. [<a href="https://arxiv.org/pdf/2105.02911">pdf</a>] Created by Fatemeh Pishdadian (1), Gordon Wichern (2), Jonathan Le Roux (2), Aurora Cramer (3, 4), Mark Cartwright (5), and Juan Pablo Bello (3,4,6,7)     1. Interactive Audio Lab, Northwestern University     2. Mitsubishi Electric Research Laboratory     3. Music and Audio Research Lab, New York University     4. Department of Electrical and Computer Engineering, New York University     5. Department of Informatics, New Jersey Institute of Technology     6. Center for Urban Science and Progress, New York University     7. Department of Computer Science and Engineering, New York University Description Synthetic noisy urban soundscapes (SNUSS) is a dataset of synthetic soundscapes with real urban background noise meant to mimic urban soundscapes. This dataset contains 30,000 10 second mixtures, their isolated components, and auto-generated annotations. This dataset was developed with the goal of synthesizing soundscapes with a diverse set of realistic sounding background activity, for use in developing and evaluating machine listening systems in urban settings. Mixture generation We generate synthetic mixtures using a collection of isolated sound events with class annotations, as well as a collection of urban background noise.  Audio mixtures are 4 seconds long (at 16kHz). We generate a foreground sub-mixture using a subset of clips from <a href="https://urbansounddataset.weebly.com/urbansound8k.html">UrbanSound8K</a> [3] from the car horn, dog bark, gun shot, jackhammer and siren classes. These clips range from 0.5 s to 4s. The number of events per mixture is sampled from a zero-truncated Poisson distribution with a rate parameter of 5. The class for each event is chosen uniformly at random from the five target classes. The particular sound event is chosen uniformly at random from the available clips for that class. The start time is chosen uniformly throughout the clip such that the entire clip is contained in the 4 second mixture. A brief fade-in and fade-out is applied to the clip to avoid discontinuities. Each clip is set to a sound level sampled uniformly at random in the range -30 to -20 dB LUFS. For the background audio, we use urban background recordings from the SONYC-Background dataset [2, 4], containing 441 10 second recordings of urban background noise in New York City. For more information, see the <a href="https://doi.org/10.5281/zenodo.5129078">SONYC-Backgrounds page</a>. For each mixture, a random background clip is chosen from which we extract a uniformly chosen 4 second segment. We create datasets using an foreground-to-background SNRs of -50, -20 -0 dB LUFS, (`n50dB`, `n20dB`, and `0dB` respectively), in addition to a noiseless dataset (`none`). The datasets are generated such that the only difference between them is the relative loudness between the foreground and background. For the training set, we generate 20,000 mixtures using folds 1-6 of UrbanSound8K and the training set of SONYC-Background. For the validation set, we generate 5,000 examples using folds 7-8 of UrbanSound8K and the validation set of SONYC-Background. For the test set, we generate 5,000 examples using folds 9-10 of UrbanSound8K and the test set of SONYC-Background. For additional details on the foreground mixture generation process, please refer to [1]. For additional details on generating the soundscapes with background, please refer to [2]. Files The dataset files are split into the following compressed archives: <ul> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-none.tar.gz` - Noiseless mixtures</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n50dB.tar.gz` - Mixtures with -50 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n20dB.tar.gz` - Mixtures with -20 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-0dB.tar.gz` - Mixtures with 0 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_isolated_events.tar.gz` - Isolated sound events for each mixture</li> </ul> Preparing the files <ol> <li>Download each of the tar.gz files to a new folder. You need at the `isolated_events` and one of the mixture datasets.</li> <li>Decompress all of the tar.gz files.</li> <li>Merge the contents of the extracted `isolated_events` folder into the extracted mixture folders. This makes sure the corresponding isolated events for each mixture are placed in its `XXXXX_events` folder.</li> </ol> File structure The mixture dataset folder for the desired background condition should have the format `synthetic-noisy-urban-soundscapes_mixtures-bkgr-<condition>/<split>`. Within each split folder are the mixture files, which are identified by an integer (padded with leading zeros up to 5 places). For a mixture `00001`, the mixture audio is `00001.wav` and the annotation file (in JAMS [5] format) is `00001.jams`. The isolated events can be found in the `00001_events` folder, where foreground events have the format `foreground<fg-event-num>_<class-name>.wav` and the background recording (if used) is called `background0_2017.wav`. Contact If you have any questions, comments, or concerns, please direct correspondence to Aurora Cramer (aurora (dot) linh (dot) cramer (at) gmail (dot) com).   References [1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP). [2] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J. P. (2021). Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). [3] Salamon, J., Jacoby, C., and Bello, J.P. (2014). A dataset and taxonomy for urban sound research. In 2014 ACM International Conference on Multimedia. [4] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. (2021). SONYC-Backgrounds: a collection of urban background recordings from an acoustic sensor network (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5129078 [5] Humphrey, E. J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R. M., and Bello, J.P. (2014). JAMS: A JSON Annotated Music Specification for Reproducible MIR Research. In 2014 International Society for Music Information Retrieval Conference (ISMIR) Acknowledgements This work is partially supported by National Science Foundation <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633259">award 1633259</a> and <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">award 1544753</a>.

opencc-by-4.0Nov 2022View details →

Diel variation in insect-dominated temperate pond soundscapes and guidelines for survey design

The data and code accompanying 'Diel variation in insect-dominated temperate pond soundscapes and guidelines for survey design' published in Freshwater Biology.

opencc-zeroJan 2023View details →

Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species

Passive acoustic monitoring (PAM) offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we addressed one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.

opencc-by-4.0Mar 2023View details →

Soundscape and small cetacean sounds. Puerto Cisnes May 2021.

Audio files recorded in Puyuhuapi sound, close to the city of Puerto Cisnes, Region de Aysen, Chile, from May, 04th 2021 to May, 06th 2021 . The files with more than 300 detected clicks of small NBHF cetaceans have been uploaded. The cetacean are probably Chilean dolphins (Cephalorhynchus eutropia), but possibly also Peale’s dolphins (Lagenorhynchus australis) or Burmeister’s porpoise (Phocoena spinipinnis). Instrument is QHB Recorder from Smiot, Université de Toulon (http://bioacoustics.lis-lab.fr/smiot), set with sample rate of 512 Ksps, sampling depth of 16 bits, and C57 Cetacean Research hydrophone. The whole dataset, too large to be included in Zenodo repository, can be found on the following adress : http://sabiod.lis-lab.fr/pub/CHILI/PUERTO_CISNES_2021/ .

opencc-zeroJul 2023View details →

Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes

Marine soundscapes provide the opportunity to non-invasively learn about, monitor, and conserve ecosystems. Some fishes produce sound in chorus, often in association with mating, and there is much to learn about fish choruses and the species producing them. Manually analyzing years of acoustic data is increasingly unfeasible, and is especially challenging with fish chorus, as multiple fish choruses can co-occur in time and frequency and can overlap with vessel noise and other transient sounds. SoundScape Learning (SSL) is a novel unsupervised automated method, to separate fish chorus from soundscape. SSL is an integrated technique that makes use of randomized robust principal component analysis (RRPCA), unsupervised clustering, and a neural network. SSL was applied to 14 recording locations off southern and central California and was able to detect a single fish chorus of interest in 5.3 yrs of acoustically diverse soundscapes. Through application of SSL, the chorus of interest was found to be nocturnal, increased in intensity at sunset and sunrise, and was seasonally present from late Spring to late Fall. Further application of SSL will improve understanding of fish behavior, essential habitat, species distribution, and potential human and climate change impacts, and thus allow for protection of vulnerable fish species. This repository provides example data and code for the JASA paper: SoundScape Learning: an automatic method for separating fish chorus in marine soundscapes.

publicFeb 2024View details →

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets

Open the record for dataset details and reuse information.

publicJan 2023View details →

Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species

Open the record for dataset details and reuse information.

publicNov 2023View details →

Rana sierrae annotated aquatic soundscapes (2022)

Open the record for dataset details and reuse information.

publicJul 2023View details →

Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2020View details →

"Deepening Presence - Probing the hidden artefacts of everyday soundscapes" sound examples

Sound examples for the paper "Deepening Presence - Probing the hidden artefacts of everyday soundscapes"

dryad36/100

Silence is sexy: Soundscape complexity alters mate choice in túngara frogs

Many animals acoustically communicate in large aggregations, producing biotic soundscapes. In turn, these natural soundscapes can influence the efficacy of animal communication, yet little is known about how variation in soundscape interferes with animals that communicate acoustically. We quantified this variation by analyzing natural soundscapes with the mid-frequency cover index and by measuring the frequency ranges and call rates of the most common acoustically communicating species. We then tested female mate choice in the túngara frog (Physalaemus pustulosus) in varying types of background chorus noise. We broadcast two natural túngara frog calls as a stimulus and altered the densities (duty cycles) of natural calls from conspecifics and heterospecifics to form the different types of chorus noise. During both conspecific and heterospecific chorus noise treatments, females demonstrated similar preferences for advertisement calls at low and mid noise densities but failed to express a preference in the presence of high noise density. Our data also suggest that nights with high densities of chorus noise from conspecifics and heterospecifics are common in some breeding ponds, and on nights with high noise density, the soundscape plays an important role diminishing the accuracy of female decision-making.

opencc-zeroAug 2020View details →

opencc-by-4.0Jan 2021View details →

A systematic literature assessment on the effects of human-altered soundscapes on marine life

A dataset containing the results of a systematic assessment of the literature on the effects of human-altered soundscapes on marine life. This identified 538 peer-reviewed papers that consider how changes in anthrophony, geophony, and biophony affect marine fauna. Papers on the effects of anthrophony are categorised by species, taxonomic group, effect category, statistical outcome, and whether mitigation strategies were tested. Papers considering changes in bio- or geo-phony contain details on the ecosystem where responses were measured, the effect shown, and the statistical outcome.

opencc-by-4.0Jan 2024View details →

Coral Reef Soundscapes - Lizard Island, Australia

Repository to support the publication: Unlocking the soundscapes of coral reefs with artificial intelligence. Williams et al (2024) https://doi.org/10.1101/2024.02.02.578582. If using this data please cite or acknowledge the paper. This repository contains all the raw data from the Australian coral reef soundscape dataset. Note, some site names were switched for the publication, so differ to the raw files in this repository. For conversions use: This repo -> The publication SiteA -> Site A SiteB -> Site G SiteC -> Site D SiteD -> Site E SiteE -> Site B SiteF -> Site I SiteG -> Site H SiteH -> Site L SiteI -> Site C SiteJ -> Site F SiteK -> Site J SiteL -> Site K

opencc-by-4.0Feb 2024View details →

Audio recordings and soundscape assessments from the study: "Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings"

<h1>Content</h1> The dataset contains processed audio files employed in a listening test performed at the Here East Audio Lab of the University College London to derive a model of acoustic perception in residential buildings [1]. The study followed a full factorial design by combining five urban environments (Factor A) and four indoor sound scenarios (Factor B). The audio files are available in both B-format (Ambix) and A-format. Furthermore, the component scores of each participant in the three derived perceptual dimensions (i.e., comfort, content, familiarity) are made available, along with the psychoacoustic analysis of 20 binaural recordings, each lasting 1 minute, corresponding to the 20 acoustic scenarios to which the 32 participants were exposed. <h1>Audio files</h1> Factor A (Outdoor sounds) Factor A audio recordings were performed in indoor spaces without audible indoor sound sources and with windows partially opened to different urban contexts in the city of London. Sound material was recorded @24bit/48kHz using a First Order Ambisonics (FOA) microphone (Sennheiser AMBEO VR Mic) positioned at the average listener’s ear height in endfire position, with accompanying portable multi-channel audio recorder (Sound Devices MixPre-10T) with channels 1-4 linked for the FOA setting, together with a sound level meter (NTi Audio XL2), both microphones oriented towards the window openings. By recording outdoor acoustic environments in indoor spaces, the effects of reverberation and window filtering were intrinsically embedded in the collected recordings. Factor B (Indoor sounds) Factor B audio recordings were made in low-noise indoor environments using the equipment described above with both microphones oriented roughly towards the sound source of interest. Combinations of Factors A & B Excluding the two “no added sounds” scenarios, a total of seven audio stimuli were played and combined during the listening tests, as described in [1], resulting in total 20 scenarios where no more than 2 sounds were overlapped. Audio Editing and Processing Audio samples were edited and processed in A format in the Digital Audio Workstation Reaper (Cockos) @24bit/48kHz. The edits were performed in terms of removing extraneous sound events by trimming the audio track and creating the necessary crossfades, in order to bring the audio material as close as possible to the scenario it represented. Audio processing was conducted using the Sennheiser Ambeo plugin to generate the B-format audio files, to be correctly spatialized using a playback system of choice. In the process of conversion to B format, the default Ambisonics Correction Filter was engaged and the Low Cut Filter was switched off, while the Microphone Rotation was set to correct for the endfire position used during the recordings. One-minute excerpts were finally extracted. No further audio editing, nor processing was done. Full details about sound recordings and playback levels used in the experiment are available in [1] and in the supplementary materials. The audio files are intended to be employed in future listening tests. <h1>Psychoacoustic Analysis and Soundscape scores (.xlsx file)</h1> The xlsx file is formatted with a row for each individual participant's component scores per each of the 20 experimental conditions, then includes the psychoacoustic analysis of the 60s binaural recording corresponding to each acoustic condition. Details about the psychoacoustic analyses and component scores derivation are provided in [1] and in the related supplementary material. In the sheet "Legend_Exposure_Conditions", the coding of the 20 conditions is provided. The numbers of the levels for factors A and B refer to Table 1 in [1].   [1] Torresin, S., Albatici, R., Aletta, F., Babich, F., Oberman, T., Siboni, S., & Kang, J. (2020). Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment, 182, 107152.

opencc-by-4.0Dec 2021View details →

Urban Soundscapes in the Imaginaries of Native Digital Users: Guidelines for Soundscape Design

Collages and narratives collected in Seoul and in Paris to illustrate the participant's experience with urban soundscapes, together with their full semantic analyses according to the analysis grid presented in the reference publication.