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4 results for “Synthetic audio dataset”
BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings
<p>BirdVox-scaper-10k: a synthetic dataset for multilabel species classification of flight calls from 10-second audio recordings<br> =============================================================================================<br> Version 1.0, September 2019.</p> <p> </p> <p>Created By<br> -------------</p> <p>Elizabeth Mendoza (1), Vincent Lostanlen (2, 3, 4), Justin Salamon (3, 4), Andrew Farnsworth (2), Steve Kelling (2), and Juan Pablo Bello (3, 4).</p> <p> </p> <p>(1): Forest Hills High School, New York, NY, USA<br> (2): Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA<br> (3): Center for Urban Science and Progress, New York University, New York, NY, USA<br> (4): Music and Audio Research Lab, New York University, New York, NY, USA</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> --------------</p> <p>The BirdVox-scaper-10k dataset contains 9983 artificial soundscapes. Each soundscape lasts exactly ten seconds and contains one or several avian flight calls from up to 30 different species of New World warblers (Parulidae). Alongside each audio file, we include an annotation file describing the start time and end time of each flight call in the corresponding soundscape, as well as the species of warbler it belongs to.</p> <p>In order to synthesize soundscapes in BirdVox-scaper-10k, we mixed natural sounds from various pre-recorded sources. First, we extracted isolated recordings of flight calls containing little or no background noise from the CLO-43SD dataset [1]. Secondly, we extracted 10-second "empty" acoustic scenes from the BirdVox-DCASE-20k dataset [2]. These acoustic scenes contain various sources of real-world background noise, including biophony (insects) and anthropophony (vehicles), yet are guaranteed to be devoid of any flight calls. Lastly, we "fill" each acoustic scene by mixing it with flight calls sampled at random.</p> <p>Although the BirdVox-scaper-10k does not consist of natural recordings, we have taken several measures to ensure the plausibility of each synthesized soundscape, both from qualitative and quantitative standpoints.<br> <br> The BirdVox-scaper-10k dataset can be used, among other things, for the research, development, and testing of bioacoustic classification models.</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] J. Salamon, J. Bello. Fusing shallow and deep learning for bioacoustic bird species classification. Proc. IEEE ICASSP, 2017.</p> <p>[2] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, and J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[3] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p> </p> <p> </p> <p> </p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p>
EmoSynth: The Emotional Synthetic Audio Dataset
<p>The ability of sound to enhance human wellbeing has been known since ancient civilisations, and methods can be found today across domains of health and within a variety of cultures. </p> <p>EmoSynth is a dataset of 144 audio files which have been labelled by 40 listeners for their the perceived emotion, in regards to the dimensions of Valence and Arousal.</p> <p>Results on the dataset show that Arousal does correlate moderately to fundamental frequency, and that the sine waveform is perceived as significantly different to square and sawtooth waveforms when evaluating perceived Arousal. The general results suggest that isolated synthetic audio can be modelled as a means of evoking affective states of emotion.</p> <p>When using this version of the data for a publication, please cite the following study: </p> <pre>@inproceedings{Baird:2018:PEI:3243274.3243277, author = {Baird, Alice and Parada-Cabaleiro, Emilia and Fraser, Cameron and Hantke, Simone and Schuller, Bj\"{o}rn}, title = {The Perceived Emotion of Isolated Synthetic Audio: The EmoSynth Dataset and Results}, booktitle = {Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion}, series = {AM'18}, year = {2018}, isbn = {978-1-4503-6609-0}, location = {Wrexham, United Kingdom}, pages = {7:1--7:8}, articleno = {7}, numpages = {8}, url = {http://doi.acm.org/10.1145/3243274.3243277}, doi = {10.1145/3243274.3243277}, acmid = {3243277}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {affective computing, perception, sound healing, synthetic audio, wellbeing}, } </pre> <p> </p>
EmotionCaps: A Synthetic Emotion-Enriched Audio Captioning Dataset
<p>Version 1.0, October 2024</p> <h2>Created by</h2> <p>Mithun Manivannan (1), Vignesh Nethrapalli (1), Mark Cartwright (1)</p> <ol> <li>Sound Interaction and Computer Lab, New Jersey Institute of Technology</li> </ol> <h2>Publication</h2> <p>If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:</p> <p>Manivannan, M., Nethrapalli, V., Cartwright, M. EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation. arXiv preprint arXiv:2410.12028, 2024.</p> <h2>Description</h2> <p>EmotionCaps is a ChatGPT-assisted, weakly-labeled audio captioning dataset developed to bridge the gap between soundscape emotion recognition (SER) and automated audio captioning (AAC). Created through a three-stage pipeline, the dataset leverages ground-truth annotations from AudioSet SL, which are enhanced by ChatGPT using tailored prompts and emotions assigned via a soundscape emotion recognition model trained on Emo-Soundscapes Dataset. It comprises four subsets of captions for 120,071 audio clips, each reflecting a different prompt variation: WavCaps-like, Scene-Focused, Emotion Addon, and Emotion Rewrite. The average word counts for these subsets are: WavCaps-like (12.61), Scene-Focused (14.04), Emotion Addon (18.35), and Emotion Rewrite (18.65). The increase in word count for the emotion prompts illustrates the difference in sentence length when integrating emotion information into the captions.</p> <h2>Audio Data</h2> <p>The audio data is from AudioSet SL, the strongly-labled subset of 120,071 audio clips from the larger AudioSet dataset.</p> <h2>Synthetic Captions</h2> <p>The synthetic captions were generated using a three-stage pipeline, beginning with training a soundscape emotion recognition model. This model assesses the valence and arousal of each audio clip, mapping the resulting vector to an emotion identifier. Next, we leveraged the ground-truth annotations from AudioSet SL, and extracted the list of sound events. Using these sound events, we employed ChatGPT to create different variations of captions by applying distinct prompts.</p> <p>We first used the WavCaps prompt for AudioSet SL as a base, the output of which we call WavCaps-like. Building on this, we created three new prompt variations (1) <strong>scene-focused</strong> which is a modified WavCaps prompt that describes the scene, (2) <strong>emotion addon</strong> which is an extension of the scene-Focused prompt, where an emotion is appended to the list of sound events to guide the caption generation, and (3) <strong>emotion rewrite</strong> which consists of two-step prompt where ChatGPT first generates the scene-focused caption, then is instructed to rewrite it with a specific emotion in mind.</p> <p>Using these four prompt styles — WavCaps, Scene-Focused, Emotion Addon, and Emotion Rewrite — along with the AudioSet SL sound events and predicted emotions, we employed ChatGPT-3.5 Turbo to generate four corresponding caption variations for the dataset.</p> <p>Each caption variation has been organized into separate CSV files for clarity and accessibility. All files correspond to the same set of audio clips from AudioSet SL, with the key distinction being the caption variation associated with each clip. The different subsets are designed to be used independently, as they each fulfill specific roles in understanding the impact of emotion in audio captions.</p> <ul> <li> <p><strong>wavcaps-like.csv</strong>: Contains captions generated using the WavCaps prompt, serving as the baseline before emotion is introduced.</p> </li> <li> <p><strong>scene-focused.csv</strong>: Provides captions focused on describing the scene or environment of the audio clip, without emotion integration.</p> </li> <li> <p><strong>emotion-addon.csv</strong>: Captions where emotion data is appended to the scene-focused base caption.</p> </li> <li> <p><strong>emotion-rewrite.csv</strong>: Captions that are completely rewritten based on the scene-focused base caption and the assigned emotion.</p> </li> </ul> <p>This structure allows users to explore how emotional content influences captioning models by comparing the variations both with and without emotional enrichment.</p> <h2>Columns in CSV files</h2> <p><em><strong>segment_id</strong></em> : The ID of the audio recording in AudioSet SL. These are in the form <em><YouTube ID>_<start time in ms>_<end time in ms></em></p> <p><em><strong>caption</strong></em> : The caption generated for each audio clip, corresponding to the specific subset (e.g., WavCaps, Scene-Focused, Emotion Addon, or Emotion Rewrite) as indicated by the file name.</p> <h2>Conditions of use</h2> <p>Dataset created by Mithun Manivannan, Vignesh Nethrapalli, Mark Cartwright</p> <p>The EmotionCaps dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:</p> <p><a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New Jersey Institute of Technology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the EmotionCaps dataset or any part of it.</p> <h2>Feedback</h2> <p>Please help us improve EmotionCaps by sending your feedback to:</p> <ul> <li>Mithun Manivannan: <a href="mailto:mithun.mani01@gmail.com">mithun.mani01@gmail.com</a></li> <li>Mark Cartwright: <a href="mailto:mcartwright@gmail.com">mcartwright@gmail.com</a></li> </ul> <p>In case of a problem, please include as many details as possible.</p> <h2>Acknowledgments</h2> <p>This work was partially supported by the New Jersey Institute of Technology Honors Summer Research Institute (HSRI).</p>
ACPAS dataset: Aligned Classical Piano Audio and Score (synthetic subset)
<p><strong>ACPAS</strong> is a dataset with aligned audio and scores for classical piano music containing 497 distinct music scores aligned with 2189 performances, in total 179.77 hours. For each performance, we provide the corresponding performance audio (real recording or synthesized recording), performance MIDI, and MIDI score, together with rhythm and key annotations.</p> <p>This is the <strong>Synthetic subset</strong> of the ACPAS dataset. To download the full dataset and for dataset details, please refer to the dataset webpage at <a href="https://cheriell.github.io/research/ACPAS_dataset">https://cheriell.github.io/research/ACPAS_dataset</a></p> <p>For any questions, suggestions, or comments, please do not hesitate to contact <a href="mailto:lele.liu@qmul.ac.uk">lele.liu@qmul.ac.uk</a></p> <p><strong>How to cite:</strong></p> <p>- Lele Liu, Veronica Morfi, and Emmanouil Benetos, "ACPAS: A Dataset of Aligned Classical Piano Audio and Scores for Audio-to-Score Transcription," in ISMIR Late-breaking Demo, 2021.</p> <p><strong>Funding:</strong></p> <p>L. Liu is a research student at the UKRI Centre for Doctoral Training in Artificial Intelligence and Music, supported jointly by the China Scholarship Council and Queen Mary University of London.</p>
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