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639 results for “Audio”
Saarland Music Data: MIDI-Audio Piano Music
<p>This is an improved version of the dataset originally referred to as <strong>SMD MIDI-Audio Piano Music.</strong> For more details, please visit the website: <a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/midi">https://www.audiolabs-erlangen.de/resources/MIR/SMD/midi</a></p> <p>Saarland Music Data provides audio recordings along with perfectly synchronized MIDI files for various piano pieces. The pieces were performed by students of the <a href="http://www.hfm.saarland.de">Hochschule für Musik Saar</a> on a hybrid acoustic/digital piano <a href="http://www.yamaha.com/Products/Disklavier.html">Yamaha Disklavier</a>. The Disklavier allows for capturing key and pedal movements of the piano while playing. This information, which can be stored in a MIDI file, yields an accurate annotation of the corresponding audio recording in form of a symbolic description of all played musical note events. The SMD MIDI-Audio pairs constitute a valuable dataset for various music analysis tasks such as music transcription, performance analysis, music synchronization, audio alignment, or source separation. </p> <p>All performances were recorded in the studios of the <a href="http://www.hfm.saarland.de">Hochschule für Musik Saar</a>, played by students of piano classes of different levels, on a <a href="http://www.yamaha.com/Products/Disklavier.html">Yamaha Disklavier</a> model <a href="http://www.yamaha.com/yamahavgn/CDA/ContentDetail/ModelSeriesDetail.html?CNTID=556850&CNTYP=PRODUCT">DCFIIISM4PRO</a>. Using two cardioid-condenser microphones fixed over the resonating body of the piano, all performances were directly recorded into Steinberg Cubase 4. Except for trimming the beginnings and ends of the recordings, no further post-processing (filters, effects) was applied to the musical material. From each Cubase project, an audio file (44.1 kHz, stereo) as well as a synchronized standard MIDI file (SMF) were exported. Besides these files, we also provide the audio files as WAV (22.05 kHz, mono) and the MIDI files encoded as CSV files and as WAV files (22.05 kHz, mono) rendered using the Software synthesizer <a href="https://www.fluidsynth.org/">FluidSynth</a>.</p> <p>SMD MIDI-Audio Piano Music (V1) contains the following data:</p> <ul> <li>wav_44100_stereo: Audio file (44.1 kHz, stereo)</li> <li>wav_22050_mono: Audio file (22.05 kHz, mono)</li> <li>midi: MIDI file</li> <li>csv: Export of note events from MIDI file into CSV format</li> <li>midi_wav_22050_mono: MIDI file rendered as audio file (22.05 kHz, mono)</li> </ul> <p>If you publish results obtained using this dataset, please cite:</p> <p>Meinard Müller, Verena Konz, Wolfgang Bogler, Vlora Arifi-Müller: Saarland Music Data (SMD). In Late-Breaking and Demo Session of the 12th International Conference on Music Information Retrieval (ISMIR), 2011. [<a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/2011_MuellerKonzBoglerArifi_SaarlandMusicData_ISMIR-LateBreaking.pdf">pdf</a>] [<a href="https://www.audiolabs-erlangen.de/resources/MIR/SMD/bibtex.html">bib</a>]</p>
WaivOps EDM-HSE: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-HSE Dataset</strong></p> <p>EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models.</p> <p><strong>Overview</strong></p> <p>The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities.</p> <p>The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>8,000 audio loops (approximately 17 hours)</li> <li>16-bit WAV format</li> <li>Tempo range: 120–130 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Big room, electro, minimal, classic)</li> </ul> <p>A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.</p> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The EDM-HSE dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects.</p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-EDM-HSE">GitHub repository</a>.</p>
WaivOps WRLD-SMB: Open Audio Resources for Machine Learning in Music
<p><strong>WRLD-SMB Dataset</strong></p> <p>WRLD-SMB is an open audio dataset featuring a collection of synthetic drum recordings in the style of Brazilian samba music. It includes 1,100 audio loops recorded in uncompressed stereo WAV format, along with paired JSON files intended for the supervised training of generative AI audio models.</p> <p><strong>Overview</strong></p> <p>This dataset was developed using multi-velocity audio samples and a paired MIDI dataset. The intended use of this dataset is to train or fine-tune AI models in learning high-performance drum notations, aiming to replicate the live sound of a small drum ensemble. To facilitate augmentation and supervised training with labeled audio data, a dropout technique was employed on the rendered audio files to generate variational mixes of the drum tracks.</p> <p>The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>1,100 audio loops (approximately 5.5 hours)</li> <li>16-bit 44.1kHz WAV format</li> <li>Tempo range: 90–120 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Traditional and modern samba, bossa nova, fusion)</li> </ul> <p>A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.</p> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The WRLD-SMB dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-WRLD-SMB">GitHub repository</a>.</p>
The language of sound search: Examining User Queries in Audio Search Engines (supplementary materials)
<h2>Overview</h2> <p>This dataset accompanies the <a href="https://dcase.community/documents/workshop2024/proceedings/DCASE2024Workshop_Weck_54.pdf" target="_blank" rel="noopener">paper</a> titled <strong>"The Language of Sound Search: Examining User Queries in Audio Search Engines."</strong> The study investigates user-generated textual queries within the context of sound search engines, which are commonly used for applications such as foley, sound effects, and general audio retrieval.</p> <p>The paper addresses the gap in current research regarding the real-world needs and behaviors of users when designing text-based audio retrieval systems. By analyzing search queries collected from two sources — a custom survey and Freesound query logs — the study provides insights into user behavior in sound search contexts. Our findings reveal that users tend to formulate longer and more detailed queries when not constrained by existing systems, and that both survey and <a href="https://freesound.org/">Freesound</a> queries are predominantly keyword-based.</p> <p>This dataset contains the raw data collected from the survey and annotations of Freesound query logs.</p> <h2>Files in This Dataset</h2> <p>The dataset includes the following files:</p> <ol> <li> <p><strong><code>participants.csv</code></strong><br>Contains data from the survey participants. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each participant.</li> <li><code>fluency</code>: Self-reported English language proficiency.</li> <li><code>experience</code>: Whether the participant has used online sound libraries before.</li> <li><code>passed_instructions</code>: Boolean value indicating whether the participant advanced past the instructions page in the survey.</li> </ul> </li> <li> <p><strong><code>annotations.csv</code></strong><br>Contains annotations of the survey responses, detailing the participants' interaction with the sound search tasks. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each annotation.</li> <li><code>participant_id</code>: Links to the participant’s ID in <code>participants.csv</code>.</li> <li><code>stimulus_id</code>: Identifier for the stimulus presented to the participant (audio, image, or text description).</li> <li><code>stimulus_type</code>: The type of stimulus (audio, image, text).</li> <li><code>audio_result_id</code>: Identifier for the hypothetical audio result presented during the search task.</li> <li><code>query1</code>: Initial search query submitted based on the stimulus.</li> <li><code>query2</code>: Refined search query after seeing the hypothetical search result.</li> <li><code>aspects1</code>: Aspects considered important when formulating the initial query.</li> <li><code>aspects2</code>: Aspects considered important when refining the query.</li> <li><code>result_relevance</code>: Participant's rating of the hypothetical search result's relevance.</li> <li><code>time</code>: Time taken to complete the search task.</li> </ul> </li> <li> <p><strong><code>freesound_queries_annotated.csv</code></strong><br>Contains annotated Freesound search queries. Columns:</p> <ul> <li><code>query</code>: Text of the search query submitted to Freesound.</li> <li><code>count</code>: The number of times the specific query was submitted.</li> <li><code>topic</code>: Annotated topic of the query, based on an ontology derived from AudioSet, with an additional category, <code>Other</code>, which includes non-English queries and NSFW-related content.</li> </ul> </li> <li> <p><strong><code>survey_stimuli_data.zip</code></strong><br>This ZIP file contains three CSV files corresponding to the three stimulus types used in the survey:</p> <ul> <li><strong>Audio stimuli</strong>: Categorized sound recordings presented to participants.</li> <li><strong>Image stimuli</strong>: Annotated images that prompted sound-related queries.</li> <li><strong>Text stimuli</strong>: Summarized descriptions of sounds provided to participants.</li> </ul> </li> </ol> <p>More details on the stimuli and the survey methodology can be found in the accompanying paper.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the corresponding paper:</p> <div> <pre>B. Weck and F. Font, ‘The Language of Sound Search: Examining User Queries in Audio Search Engines’, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024), Tokyo, Japan, Oct. 2024, pp. 181–185.</pre> <pre><code>@inproceedings{Weck2024, author = "Weck, Benno and Font, Frederic", title = "The Language of Sound Search: Examining User Queries in Audio Search Engines", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024)", address = "Tokyo, Japan", month = "October", year = "2024", pages = "181--185" }</code></pre> </div>
WaivOps SYN-SE1: Open Audio Resources for Machine Learning in Music
<div> <p><strong>SYN-SE1 Dataset</strong></p> <p>SYN-SE1 is an open audio dataset containing archived recordings of a Studio Electronics SE1 analog synthesizer. It includes 1,000 one-shot audio samples recorded in uncompressed stereo WAV format, labeled by note key across a two-octave range. The presets encompass a variety of distinct synth bass and lower-pitched lead sounds, featuring filter modulations and spatial stereo imaging, providing a valuable resource for soundfont design, audio production, and training data for generative AI models.</p> <p>The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include pitch detection, musical note classification, audio synthesis, music information retrieval (MIR), sound design, and signal processing.</p> <br><strong>Specifications</strong> <ul> <li>1,000 audio samples</li> <li>16-bit WAV format</li> <li>Key note labeled samples</li> <li>JSON reference</li> <li>Analog synth bass and leads</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed and published by Patchbanks. All recordings have been obtained from verified sources to ensure copyright clearance.</p> <p>The SYN-SE1 dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-SYN-SE1" target="_blank" rel="noopener">GitHub repository</a>.</p> </div>
WaivOps POP-ROK: Open Audio Resources for Machine Learning in Music
<div> <p><strong>POP-ROK Dataset</strong></p> <p>POP-ROK is an open audio dataset featuring an uncurated collection of synthetic drum recordings in the style of pop rock music. It includes 5,378 audio loops recorded in uncompressed stereo WAV format, along with paired JSON files intended for the supervised training of generative AI audio models.</p> <p><strong>Overview</strong></p> <p>The POP-ROK Dataset was developed by sonifying a collection of approximately 30 acoustic drum kits with a paired MIDI dataset covering basic rhythm patterns, excluding toms. Data augmentation included a random drum-swapping method to generate unique drum kits and reverb simulations to represent various room sizes. This dataset is intended for training or fine-tuning AI models in rhythm notation with paired drum note labels, aiming to replicate the sound of live drumming.</p> <p>The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>5,378 audio loops (approximately 24 hours)</li> <li>16-bit WAV format</li> <li>Tempo range: 100-130 BPM</li> <li>Paired label data (WAV + JSON)</li> <li>Variational drum patterns</li> <li>Subgenre styles (Pop, classic rock, soft rock, country)</li> </ul> <p>A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.</p> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The POP-ROK dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-POP-ROK" target="_blank" rel="noopener">GitHub repository</a>.</p> </div>
CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription
<p>We collect and release CrowdSpeech — the first publicly available large-scale dataset of crowdsourced audio transcriptions. e show its applicability on an under-resourced language by constructing VoxDIY — a counterpart of CrowdSpeech for the Russian language.</p>
Audio features for DSL-S 2023 shared task
<p>These files are features (i-vectors, x-vectors, MFCC features) extracted from the subset of <a href="https://commonvoice.mozilla.org/">Mozilla Common Voice</a> corpus version 12.0 used in the <a href="https://sites.google.com/view/vardial-2023">VarDial 2023</a> shared task on <a href="https://dsl-s.github.io/">Discriminating Between Similar Languages - Speech</a> (DSL-S 2023).</p> <p>This data set contains only the features for the training and development section (and the Common Voice meta data) for the nine languages included in the shared task (see shared task website for further information).</p> <p> </p>
Audio files of synthetic sustained vowels for the study of vocal fry
<p>This is the dataset of stimuli used in the experiments reported in [1]. SinglePulsing.zip contains the stimuli of reported experiment 1, Transition.zip contains stimuli of experiment 2.</p> <p></p> <p>[1] V. Devaraj, F. Wendt, I. Roesner, J. Schoentgen, and P. Aichinger, “Auditory perception of impulsiveness and tonality in vocal fry,” <em>Appl. Sci. Basel</em>. (under review)</p>
Project's repository for: Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm
<p>Repository of the VR scene and the audio data used for the experiment reported in the publication <a href="https://ieeexplore.ieee.org/document/10269056" target="_blank" rel="noopener">available in Open Access</a>:</p> <blockquote> <p>Davide Fantini, Giorgio Presti, Michele Geronazzo, Riccardo Bona, Alessandro Giuseppe Privitera and Federico Avanzini (2023) "Co-immersion in Audio Augmented Virtuality: the Case Study of a Static and Approximated Late Reverberation Algorithm" in <em>IEEE Transactions on Visualization and Computer Graphics (ISMAR special issue)</em></p> </blockquote> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/README.md">README.md</a> includes some instructions to use the data in this repository.</p> <p> </p> <p><strong>AUDIO</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> includes the Reaper's projects and audio files used in the experiment to provide the auditory stimuli (simultaneous reverberated speeches) to the participants. Each subfolder corresponds to a different Virtual Acoustics Environment (VAE):</p> <ul> <li><<em>LivingRoom</em>|<em>MARCo</em>|<em>METU</em>> <ul> <li><<em>Living Room</em>|<em>MARCo</em>|<em>METU</em>><em>.rpp</em>: Reaper's project for the VAE</li> <li><em>Bin</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(B\) for each source position in the VAE</li> <li><em>Freeverb</em>: folder including the speech data convolved with the late reverberation part of the reverb condition \(F_\text{d}\) for each source position in the VAE</li> <li><em>HOA</em>: <ul> <li><em>ER</em>: folder including the speech data convolved with the early reflections part (HOA in A-format) of the reference reverb condition \(H\) for each source position in the VAE</li> <li><em>Ref</em>: folder including the speech data convolved with the entire reference reverb condition \(H\) (HOA in A-format) for each source position in the VAE</li> </ul> </li> </ul> </li> </ul> <p>The reverberated speech data in the <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/audio.zip">audio.zip</a> file are obtained using third-party datasets:</p> <ul> <li>The anechoic speech data are retrieved from four speakers (F2, F5, M3, M6) of the <a href="https://doi.org/10.5281/zenodo.6257551">ACE challenge corpus</a></li> <li>The Room Impulse Responses (RIR) in High-Order Ambisonics (HOA) format used to reverberate the speeches are retrieved from: <ul> <li><a href="https://doi.org/10.5281/zenodo.5747753">Living Room</a></li> <li><a href="https://doi.org/10.5281/zenodo.3477602">Concert hall (MARCo)</a></li> <li><a href="https://doi.org/10.5281/zenodo.2635758">Classroom (METU)</a></li> </ul> </li> </ul> <p> </p> <p><strong>VR SCENE</strong></p> <p>The file <a href="../api/files/06c374e2-c54d-40f1-ae23-c4c7afbfba5b/VRscene.zip">VRscene.zip</a> includes the Virtual Reality (VR) scene provided to the participants during the experiment via an Oculus Quest 2. This file includes two subfolders:</p> <ul> <li><em>UDPServer</em>: C# code for the UDP server used for sending the OSC messages for head tracking <ul> <li><em>external/SharpOSC.dll</em>: external library (<a href="https://github.com/ValdemarOrn/SharpOSC">SharpOSC</a>) used to interact with the OSC protocol</li> </ul> </li> <li><em>VR_Headtracking</em>: folder including the Unity project with the VR scene</li> </ul> <p> </p>
Calling activity of Birds in the White Mountain National Forest: Audio Recordings (2016 and 2018)
We collected 410 10-minute sound recordings of birds in and near the Hubbard Brook Experimental Forest in New Hampshire. Recordings, which encompassed most of the bird breeding season in each of two years, included 130,776 vocalizations from 46 taxa. In the associated publication, we report species lists, rarefaction curves, and vocalization descriptions. We also provide analyses of habitat associations, phenology, and spatial patterning in vocalization activity. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Ebutius Dilemma characters audio narrative files
<p>Audio files for Ebutius and Calle characters and for narrator created for the onsite and virtual verson of EMOTIVE project Ebutius's Dilemma experience, created for the Antonine Wall Gallery of the Hunterian Museum. More information can be found here: https://emotiveproject.eu/index.php/what-we-do/experiences/</p>
ESSENTIA analysis of audio snippets from the Million Song Dataset Taste Profile subset
<p>This upload includes the ESSENTIA analysis output of (a subset of) song snippets from the Million Song Dataset, namely those included in the Taste Profile subset. The audio snippets were collected from 7digital.com and were subsequently analyzed with ESSENTIA 2.1-beta3. Pre-trained SVM models provided by the ESSENTIA authors on their website were applied.</p> <p>The file <strong>msd_song_jsons.rar </strong>contains the ESSENTIA analysis output after applying the SVM models for highlevel feature extraction. Please note that these are 204317 files.</p> <p>The file <strong>msd_played_songs_essentia.csv.gz </strong>contains all one-dimensional real-valued fields of the jsons merged into one csv file with 204317 rows.</p> <p>The full procedure and subsequent analysis is described in</p> <p>Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. <em>Psychology of Music</em>, 0305735619868280.</p>
Audio captioning DCASE 2020 evaluation (testing) split
<p>This is the <strong>evaluation split for Task 6, Automated Audio Captioning, in DCASE 2020 Challenge</strong>. </p> <p>This evaluation split is the Clotho testing split, which is thoroughly described in the corresponding paper: </p> <p><em>K. Drossos, S. Lipping and T. Virtanen, "Clotho: an Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736-740, doi: 10.1109/ICASSP40776.2020.9052990.</em></p> <p>available online at: <a href="https://arxiv.org/abs/1910.09387">https://arxiv.org/abs/1910.09387</a> and at: <a href="https://ieeexplore.ieee.org/document/9052990 ">https://ieeexplore.ieee.org/document/9052990 </a></p> <p>This evaluation split is meant to be used for the purposes of the Task 6 at the scientific challenge DCASE 2020. This split it is not meant to be used for developing audio captioning methods. For developing audio captioning methods, you should use the development and evaluation splits of Clotho. </p> <p>If you want the development and evaluation splits of Clotho dataset, you can find them also in Zenodo, at: <a href="https://zenodo.org/record/3490684">https://zenodo.org/record/3490684</a></p> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== License ==</strong></p> <p>The audio files in the archives:</p> <ul> <li>clotho_audio_test.7z </li> </ul> <p>and the associated meta-data in the CSV file:</p> <ul> <li>clotho_metadata_test.csv</li> </ul> <p>are under the corresponding licences (mostly CreativeCommons with attribution) of Freesound [1] platform, mentioned explicitly in the CSV file for each of the audio files. That is, each audio file in the 7z archive is listed in the CSV file with the meta-data. The meta-data for each file are: </p> <ul> <li>File name</li> <li>Start and ending samples for the excerpt that is used in the Clotho dataset</li> <li>Uploader/user in the Freesound platform (manufacturer)</li> <li>Link to the licence of the file</li> </ul> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== References ==</strong><br> [1] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245</p>
Audio data from thesis Perception and Production of Nanning Mandarin Fourth Tone
<p>Recordings of 4 female speakers (S1, S2, S3, and S4) of Nanning-accented Mandarin Chinese reading preconstructed sentences. Recordings of those 4 female speakers telling a story based on 5 pages from Mercer Mayer's wordless picture book <em>Frog on His Own</em> (FOHO). Recording of perception test and perception test warm-up given to 26 Chinese living in Nanning, Guangxi. List of corpus sentences read, test prompts, and test sheet.</p>
Nyokon word list audio recordings
<p>This deposit contains the original recorded lists of words of the Nyokon language (ISO 639-3: nvo) gathered in the process of writing the following publication: </p> <p>Lovestrand, Joseph. 2011. Notes on Nyokon phonology (Bantu A.45, Cameroon). SIL Cameroon. http://silcam.org/download.php?stid=&folder=documents&file=Nyokon_notes_phonology-Lovestrand_2011.pdf.</p> <p>A PDF copy of the publication is included in the deposit [Notes on Nyokon phonology - Lovestrand (October 2011).pdf]</p> <p>Rough phonetic transcriptions and annotations of the word lists are in SML format in the file: [Nyokon-dekereke.txt]</p> <p>This file was designed in the freeware Dekereke program, available online at: https://casali.canil.ca/</p> <p>The audio recordings can be divided into four categories. </p> <p>1. 1700-item word list</p> <p>Files named with a range of four-digit numbers, e.g. 0001-0005.wav, are the original audio files. The numbers refer to the "SIL comparatige African wordlist". Not all 1700 items were elicted and recorded. </p> <p>Roberts, James & Keith Snider. 2006. SIL comparative African wordlist (SILCAWL). SIL <br> Electronic Working Papers 2006-005. 49. http://www.sil.org/silewp/abstract.asp?ref=2006-005</p> <p>2. Individual words for Dekereke </p> <p>Files name with a single four-digit number followed by an English gloss, e.g. 0001 body, are audio clips taken from the origial recordings so that the clips can be listened to in the Dekereke program. <br> Not all words were clipped for this purpose. The recordings of many words are only availabel in the original recordigns.</p> <p>3. Contrastive Pairs </p> <p>Files named ContrastivePairs followed by a number are files that were recorded later in the analysis to check on assumed and suspected phonemic contrast. Most of the words repeat those found elsewhere. </p> <p>4. Consent </p> <p>There are a number of files recording the oral consent of the Nyokon speakers who participated in the word list elicitation sessions. <br> As acknolwedged in Lovestrand (2011) the Nyokon speaker who participated are:</p> <p>NGOUNG Isaac (chief of Ambann)<br> NGAGNI Amos Jules (chief of Ahoung)<br> AMBANG Maurice<br> EMBOM Pierre<br> ENAM Samuel<br> FOUTH Brice Rodrigue<br> HEU Emmanuel<br> HEU Paul<br> IMBO Hermine Dorothée<br> KAMANDA Jean Achille<br> KIARI André Jules<br> KOUMA Fanny<br> MBIRNANG Thomas Blaise<br> MOUOL Catherine<br> NGAGNI Emmanuel<br> YAMBASA André</p>
A studyforrest extension, an annotation of spoken language in the German dubbed movie ``Forrest Gump'' and its audio-description (annotation)
<p>This dataset contains the annotation of speech spoken in the research cut (Hanke et al. 2014; Hanke et al., 2016) of the movie "Forrest Gump" (Zemeckis, 1994) and its audio-description that was broadcast as an additional audio track (Koop et al., 2009) for visually impaired listeners on Swiss public television. The corresponding paper is hosted on github (https://github.com/psychoinformatics-de/studyforrest-paper-speechannotation) and published in f1000research (https://doi.org/10.12688/f1000research.27621.1).</p>
Dataset Interactive Audio Augmented Reality in Participatory Performance _Please Confirm You Are Not A Robot_
<p>This dataset gathers the different data from the study "Interactive Audio Augmented Reality in Participatory Performance". </p>
Audio Un-mixing Dataset (addendum)
<p>Listening test code and data supplementing the archive generated as part of the PhD research project "Improving the perceptual quality of single-channel blind audio source separation" by Toby Stokes.</p> <p>Further project details can be found at http://iosr.uk/unmixing</p> <p><strong>References</strong></p> <p>T.Stokes, "Improving the perceptual quality of single-channel blind audio source separation", PhD thesis, University of Surrey, UK, 2015</p>
Two datasets with user generated audio recordings
<p>We provide two open access datasets of <strong>user generated audio recordings </strong>captured with mobile devices such as smartphones and portable cameras . The provided audio files originate from two different public events, a <strong>musical concert</strong> and a <strong>football match</strong>. Also, for each event, we provide two different types of collections; the original <strong>unorganized</strong> collection of uncompressed audio files and an additional <strong>organized</strong> collection, where the different recordings corresponding to similar parts of the event are grouped into specific folders and time-aligned so they can be played back in unison. The interested researcher is invited to read the accompanying paper "<em>Two open access datasets of user generated audio recordings</em>" for finding out more details about these datasets and the way that they can be useful in the context of research related to the organization and reproduction of user generated content.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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