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opencc-by-4.0Dec 2023View details →

Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music

This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument. The dataset consists of two parts: <ol> <li> Experiment: Musicians’ self-reported ratings, audio recordings, and physiological recordings where the 11 expert musicians were asked to play at least 4~2-minute unfamiliar (non-popularly known) musical pieces. Participants were asked to play at least once one of the following  emotions:  happiness, sadness, relaxation, and anger. For the  physiological recordings EEG, ECG, and GSR signals were recorded. Each musican’s data is denoted by MS_ followed by the order in which they were recorded. </li> <li> Self-report questionnaire: A self-assessment questionnaire and their answers where 11 expert musicians were asked to rate musical pieces recorded based on: <ol> <li> Objective valence & arousal they felt the piece had; </li> <li> Felt valence & arousal during playing. </li> </ol> </li> </ol>   For a more detailed explanation of the dataset, its recording procedure, and its contents, see  L. Turchet, B. O'Sullivan, R. Ortner & C. Gugher (2024). Emotion Recognition of Playing Musicians from EEG, ECG, and Acoustic Signals. IEEE Transactions on Human-Machine Systems.   <h2>File Listing</h2> The following files are available (each explained in more detail below): <div> <table> <tbody> <tr> <td> Name </td> <td> Format </td> <td> Contents </td> </tr> <tr> <td> EEG_ECG_data_for_each_musician </td> <td> mat </td> <td> This folder contains the raw EEG, ECG, & GSR data for all 11 musicians for each piece they played as well as a resting state recording, which was recorded while a neutral audio stimulus was played. </td> </tr> <tr> <td> audio_data_for_each_musician </td> <td> wav, JSON, csv </td> <td> This folder contains 3 subfolders: 1) wav_audio_files_original: This is the raw audio data recorded for each musician and includes all 56 pieces included in the paper reported above, please see below regarding rejected trials. 2) wav_audio_files (original split_into_3_parts): Here, the 56 pieces are appropriately split into 3 separate parts. 3) analysis_audio_files: This folder contains the acoustic features extracted, 1714 acoustic features were extracted from each split trial. Each result is stored in .JSON, however, the collated results can be seen in all_results.csv. </td> </tr> <tr> <td> self_report_questionnaire </td> <td> pdf, xls </td> <td> Two files exist in this folder: 1. The self-reported questionnaire given to the musicians of the questionnaire during the experiment. 2.  </td> </tr> </tbody> </table> </div> <h2>File Details</h2>   <h3>EEG_ECG_data_for_each_musician</h3> These are the original raw data recordings. EEG data were recorded using a g.GAMMAcap2 by g.tec Medical Engineering, a 64-channel cap with g.SCARABEO active electrodes, with two g.GAMMAsys reference active ear clip electrodes. Two g.GAMMAbox electrode connector boxes were used to connect the active electrodes to two g.USBamp biosignal amplifiers with a sampling frequency of 256 Hz. The following 31 EEG channels were used: Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F3, F4, F7, F8, Cz, C3, C4, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6, P7, P8, PO7, PO8, O1, and O2. AFz was used as a ground electrode and Cz was used as a re-reference electrode. The right-side ear clip electrode was used as a reference electrode.  ECG data were recorded using a single g.GAMMAclip active electrode clip connected directly to the g.GAMMAbox, sharing the same ground electrode with the EEG cap and placed on position V4 of the subjects. GSR data was recorded using the g.GSRsensor² box which contains two small dry electrodes placed underneath the participant’s toes (due to the amount of hand movement required for playing an instrument). The g.GSRsensor² was connected directly to a g.GAMMAbox, using jumper cables connected to different ports in the g.USBAMPs to share the same reference and ground electrodes as the EEG cap.    The locations of the channels and their corresponding number in the raw data is as follows: <div> <table> <tbody> <tr> <td> Channel Number </td> <td> Channel Name </td> </tr> <tr> <td> 1 </td> <td> Time series </td> </tr> <tr> <td> 2 </td> <td> AF3 </td> </tr> <tr> <td> 3 </td> <td> AF4 </td> </tr> <tr> <td> 4 </td> <td> AF7 </td> </tr> <tr> <td> 5 </td> <td> AF8 </td> </tr> <tr> <td> 6 </td> <td> CP3 </td> </tr> <tr> <td> 7 </td> <td> CP4 </td> </tr> <tr> <td> 8 </td> <td> CP5 </td> </tr> <tr> <td> 9 </td> <td> CP6 </td> </tr> <tr> <td> 10 </td> <td> P1 </td> </tr> <tr> <td> 11 </td> <td> P2 </td> </tr> <tr> <td> 12 </td> <td> P5 </td> </tr> <tr> <td> 13 </td> <td> P6 </td> </tr> <tr> <td> 14 </td> <td> P7 </td> </tr> <tr> <td> 15 </td> <td> P8 </td> </tr> <tr> <td> 16 </td> <td> O1 </td> </tr> <tr> <td> 17 </td> <td> O2 </td> </tr> <tr> <td> 18 </td> <td> Cz </td> </tr> <tr> <td> 19 </td> <td> Fp1 </td> </tr> <tr> <td> 20 </td> <td> Fp2 </td> </tr> <tr> <td> 21 </td> <td> F3 </td> </tr> <tr> <td> 22 </td> <td> F4 </td> </tr> <tr> <td> 23 </td> <td> F7 </td> </tr> <tr> <td> 24 </td> <td> F8 </td> </tr> <tr> <td> 25 </td> <td> C3 </td> </tr> <tr> <td> 26 </td> <td> C4 </td> </tr> <tr> <td> 27 </td> <td> P3 </td> </tr> <tr> <td> 28 </td> <td> P4 </td> </tr> <tr> <td> 29 </td> <td> PO7 </td> </tr> <tr> <td> 30 </td> <td> PO8 </td> </tr> <tr> <td> 31 </td> <td> AFz </td> </tr> <tr> <td> 32 </td> <td> GSR </td> </tr> <tr> <td> 33 </td> <td> ECG </td> </tr> </tbody> </table> </div> <h3>audio_data_for_each_musician/wav_audio_files_original</h3> This folder contains all of the raw audio files recorded during the experiment. All pieces were recorded using the software Audacity and exported as WAV files encoded with a bit depth of 32-bits and a sampling rate of 44.1 kHz. 56 of the included trials are present in this folder.   <h3>audio_data_for_each_musician/wav_audio_files (original split_into_3_parts)</h3> This folder contains the above mentioned 56 raw audio pieces separated into 3 appropriately sized recordings.   audio_data_for_each_musician/analysis_audio_files This folder contains the 1714 acoustic features from each of the 3 separated trials from 56 accepted pieces, these are denoted by MS_ followed by the order in which the musicians were recorded and the order in which the trails were split, the individual features are in JSON format. Within this folder, we have collated all results in a csv file (all_results.csv) where the columns show the intended emotion, trial name and number, followed by the names of the acoustic features.    self_report_questionnaire This pdf is the questionnaire which each musician was given following each trial relating to the emotions communicated and felt during each trial. Most* questions in the questionnaire were multiple-choice and speak pretty much for themselves. The answers for which were collated and are described below.   self_report_questionnaire_anwsers This .xls file contains the results of all the musicians self-reported ratings from the above described questionnaire.  <div> <table> <tbody> <tr> <td> Column name </td> <td> Description </td> </tr> <tr> <td> Subject </td> <td> The subject code of the musician, denoted by MS_ </td> </tr> <tr> <td> recording_filename </td> <td> The name of the trial denoted by MS_01_ followed by the trial number. </td> </tr> <tr> <td> intended_emotion </td> <td> The intended emotion the musician was instructed to communicate. The emotions are as follows: <ol> <li> Angry </li> <li> Sad </li> <li> Relaxed </li> <li> Happy </li> </ol> They are intended to be reported by using the valence-arousal space. </td> </tr> <tr> <td> valence_communicated </td> <td> The valence rating (integer between 1 and 5), that participants were asked to objectively rate how they thought the music played would be perceived.  </td> </tr> <tr> <td> arousal_communicated </td> <td> As above, but relating to arousal rather than valence. </td> </tr> <tr> <td> valence_felt </td> <td> The valence rating (integer between 1 and 5), that participants were asked to rate how they felt while playing the piece. </td> </tr> </tbody> </table> </div>   <div> <table> <tbody> <tr> <td> arousal_felt </td> <td> As above, but relating to arousal rather than valence. </td> </tr> <tr> <td> instrument_played </td> <td> The instrument played for each trial. </td> </tr> </tbody> </table> </div>

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-4.0Nov 2024View details →

Lotte Lehmann: Santa Barbara Farewell Recital with Gwendolyn Koldofsky (Audio Recording from 1951)

21 .wav files of one of Lotte Lehmann's two farewell recitals from 1951. This performance took place in Santa Barbara with pianist Gwendolyn Koldofsky and was first publicly released in 1977 as a historical album with limited distribution by Canadian entrepreneur ‘Sam the Record Man’ Sam Sniderman after being unearthed by Lehmann’s former student, Katherine Duke. It is no longer commercially accessible, but a copy was found in the personal effects of Alan Smith at the University of Southern California by Elvia Puccinelli and it was digitized by David Huff, sound preservationist at the University of North Texas. It is published here in the context of the book/digital collection 'Accompaniment in America: Contextualizing Collaborative Piano' by Chanda VanderHart et al. (Routledge, 2025)

opencc-by-4.0Aug 2022View details →

Audio Examples for "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies"

This is a collection of audio examples of the processing corresponding to the master thesis "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies" The examples (ambisonics and binaural) contain recordings of moving sources in an anechoic chamber, which were made within different scenes partly containing noise barriers. Furthermore the simulation of noise barriers was implemented using directional filtering within a plane wave decomposition of the signals in sh domain.

opencc-zeroSep 2022View details →

ARU audio recordings with ruffed grouse annotations (Pennsylvania, 2020)

This dataset contains audio and annotations for ruffed grouse (Bonasa umbellus) drumming events. Audio is organized into subfolders by date (4 dates) and by SD card (each of the 56 SD cards was used to record data from an AudioMoth acoustic recorder in Pennsylvania in 2020). For each day and SD card there is one 5-minute audio file. The table in the annotations folder contains presence/absence (ie 1 or 0) labels for each minute (eg 0-60 seconds, 60-120, ...) of each audio file. Additionally, the annotations folder contains subfolders with annotation text files created in raven specifying the time and frequency bounds of each annotated ruffed grouse drumming event. Audio files with zero annotations do not have an annotation .txt file, except one. The file names and directory structure of Raven annotation files and audio files are an exact match, except for the file extensions (.wav vs .Table.1.selections.txt). This data was used to evaluate an automated recognition method designed to detect ruffed grouse drumming (Lapp et al 2022) Lapp, S., Larkin, J., Parker, H., Larkin, J., Shaffer, D., Tett, C., McNeil, D., Fiss, C., Kitzes, J., In Review. "Automated recognition of Ruffed Grouse drumming in field recordings". Wildlife Society Bulletin.

opencc-by-nc-4.0May 2024View details →

Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past

IMPORTANT NOTE The song B-A-B-Y was written by Isaac Hayes and David Porter, then also arranged by Booker T. Jones and Steve Cropper, and performed by Carla Thomas in 1966. The following binaural sound remixes of B-A-B-Y have been performed in a research context, with four various HRTF profiles coming from the Listen HRTF Database by Ircam, for the AES research paper “Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past”, presented at the 156th AES Convention on June 15-17, 2024, in Madrid, Spain.  I, the undersigned Jean Viardot, the first author of this paper, testify that nothing about the composition, the arrangement, or any element of the musical content of the song, has been manipulated to obtain this remix. Only the sound rendering of a digitalized version of the original master has been manipulated and modified to address the research questions being stated in the abovementioned paper. To date (5/11/2024), I have not made any profit with this remix, and I forbid anyone to publish it without my permission. For any intention to publish, broadcast, or market this remix, please contact me beforehand.

opencc-by-nc-sa-4.0Jun 2018View details →

"The Mermaid's Tears" object-based audio recording

This file is an object-based audio recording of "The Mermaid's Tears" - an interactive and immersive radio drama produced by the BBC as part of the ORPHEUS project. In the drama, you can choose to follow one of three characters (Lesley, Dee or Bill), each of which have a different audio mix. This is a BW64 file consisting of 15 audio tracks and a chunk of descriptive ADM metadata. The metadata describes 43 audio objects that make up the drama, and describes three different ways to mix those objects - one for each of the characters. You can listen to the drama through <a href="http://mermaidstears.ch.bbc.co.uk/">this website</a>.

opencc-by-4.0Oct 2024View details →

POLIPHONE: A Dataset for Smartphone Model Identification from Audio Recordings

When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution problem has been addressed in different domains, from identifying the camera model used to capture specific photographs to detecting the synthetic speech generator or microphone model used to create or record given audio tracks. Recent advancements in this area rely heavily on machine learning and data-driven techniques, which often outperform traditional signal processing-based methods.  However, a drawback of these systems is their need for large volumes of training data, which must reflect the latest technological trends to produce accurate and reliable predictions. This presents a significant challenge, as the rapid pace of technological progress makes it difficult to maintain datasets that are up-to-date with real-world conditions. For instance, in the task of smartphone model identification from audio recordings, the available datasets are often outdated or acquired inconsistently, making it difficult to develop solutions that are valid beyond a research environment. In this paper we present POLIPHONE, a dataset for smartphone model identification from audio recordings. It includes data from 20 recent smartphones recorded in a controlled environment to ensure reproducibility and scalability for future research. The released tracks contain audio data from various domains (i.e., speech, music, environmental sounds), making the corpus versatile and applicable to a wide range of use cases. We also present numerous experiments to benchmark the proposed dataset using a state-of-the-art classifier for smartphone model identification from audio recordings.

opencc-by-4.0Mar 2022View details →

Agile and the Long Crisis of Software (Audio Recording)

From <a href="https://logicmag.io/clouds/agile-and-the-long-crisis-of-software/">the article</a>: "I  began to explore the history of Agile. What I discovered was a long-running wrestling match between what managers want software development to be and what it really is, as practiced by the workers who write the code. Time and again, organizations have sought to contain software’s most troublesome tendencies—its habit of sprawling beyond timelines and measurable goals—by introducing new management styles. And for a time, it looked as though companies had found in Agile the solution to keeping developers happily on task while also working at a feverish pace. Recently, though, some signs are emerging that Agile’s power may be fading. A new moment of reckoning is in the making, one that may end up knocking Agile off its perch."

ClinicalTrials.gov36/100

Audio-recorded Gut-Hypnotherapy for Sleep and Pain in Pediatric Abdominal Pain Disorders

ClinicalTrials.gov study NCT07216092. IPD Sharing: NO. Countries: 1. Publications: 14.

publicApr 2021View details →

An annotated set of audio recordings of Eastern North American birds containing frequency, time, and species information

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publicSep 2022View details →

ARU audio recordings with ruffed grouse annotations (Pennsylvania, 2020)

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publicFeb 2021View details →

Audio recordings of Atelpus varius calls from Panama

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zenodo32/100

Audio Recordings of the Tujia Language

These audio recordings represent the collection I amassed during my fieldwork this past summer in both the Northern and Southern Tujia regions. They encompass a wide range of content, including individual word recordings, stories, oral narratives about local agricultural practices, and conversations.

opencc-by-4.0Sep 2023View details →

zenodo32/100

Audio Swarm supplementary material - audio/video recordings

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opencc-by-nc-sa-2.0May 2024View details →

zenodo32/100

Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures - Impulse Responses and Scene Recordings

This database contains recordings of impulse responses (IRs.zip) and virtual acoustic scenes (scenes.zip), with different 2D and 3D multichannel loudspeaker rendering methods. This upload contains conplementary data to [1]. The virtual acoustic scenes are a 'concert' of an orchestra with approximately 60 primary sound sources [2] in a reverberant room, a 'speech' scene with a single talker in the same room, and a 'street' scene with static and moving sound sources. Recordings were performed with a G.R.A.S. 45BB Head and Torso Simulator (Kemar) placed in the center of the loudspeaker array in the lab, with ears at 1.60 m height. Recordings include the left and right ear channel. The simulator was equipped with large anthropometric pinnae of type KB5001. All recordings are provided for each rendering method that was applied in the study [1]. Impulse responses were recorded using sine sweeps [3], sampling frequency was 44100 Hz. The impulse responses were truncated to 1.2 s. The scenes were recorded with a sampling frequency 44100 Hz.   Files are named with the following convention: filetype_scene_renderingmethod_source.[mat/wav]   References: [1] M. Gerken, V. Hohmann, G. Grimm, "Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures," Acta Austica 2024, in press. [2] C. Böhm, D. Ackermann, and S. Weinzierl, "A Multi-channel Anechoic Orchestra Recording of Beethoven's Symphony No. 8 op. 93," Journal of the Audio Engineering Society, vol. 68, no. 12, pp. 977–984, Jan. 2021, doi: 10.17743/jaes.2020.0056. [3] A. Farina, "Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique," in Audio Engineering Society Convention 108, Feb. 2000. [Online]. Available: http://www.aes.org/e-lib/browse.cfm?elib=10211

opencc-by-4.0Oct 2023View details →

MENCORE-2: Audio Recordings to Improve Decision-making in Advanced Prostate Cancer

ClinicalTrials.gov study NCT05127850. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

HEARt Sounds: Audio Recordings to Improve Discharge Communication for Cardiology Inpatients

ClinicalTrials.gov study NCT03735342. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Audio Recording During Laparoscopic Surgery

ClinicalTrials.gov study NCT03425175. IPD Sharing: NO. Countries: 1. Publications: 1.

Audio-Recorded Messages Delivered Via iPad to Prevent Delirium in Hip Fracture Patients

ClinicalTrials.gov study NCT07396532. IPD Sharing: NO. Countries: 1. Publications: 2.