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ShareScore release 0.7.1
Dataset results
39 results for “Learning resource”
Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs - Dataset
<p>Dataset providing a set of measurement of performance and power consumpetion of a virtualized Base Station (srseNB).</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>
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>
WaivOps EDM-TR9: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-TR9 Dataset</strong></p> <p>EDM-TR9 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the distinctive sounds and rhythm patterns of the Roland TR-909 drum machine within the subgenres of dance, house and techno music. The dataset contains 3780 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>3780 audio loops (approximately 8 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 120–140bpm</li> <li>Variational drum patterns</li> <li>EDM drum rhythms</li> </ul> <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-TR9 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-EDM-TR9">GitHub repository</a>.</p>
China's transboundary water resources estimation using machine learning approaches
<p>China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).</p>
WaivOps EDM-TR8: Open Audio Resources for Machine Learning in Music
<p><strong>EDM-TR8 Dataset</strong></p> <p>EDM-TR8 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the iconic sounds of the Roland TR-808 drum machine with additional electro synth drums. The dataset contains 3,790 audio loops recorded in uncompressed stereo WAV format, generated with custom audio samples and a MIDI dataset used for training symbolic music models.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>3790 audio loops (approximately 9 hours)</li> <li>16-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 95–130bpm</li> <li>Variational drum patterns</li> <li>Multi-genre rhythm styles</li> </ul> <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-TR8 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-EDM-TR8">GitHub repository</a>.</p>
WaivOps RGTM-PNO: Open Audio Resources for Machine Learning in Music
<p><strong>RGTM-PNO Dataset</strong></p> <p>RGTM-PNO is an open audio dataset featuring a collection of vintage piano songs in the style of ragtime, a genre that flourished around the turn of the 20th century. The dataset contains 262 audio tracks recorded in uncompressed stereo WAV format, synthetically generated using a custom soundfont and MIDI files sourced from public resources online.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include audio classification, automatic music transcription (ADT), music information retrieval (MIR), melody analysis, AI music generation, sound design and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>262 piano songs (approximately 13.5 hours)</li> <li>16-bit WAV format</li> <li>Tempo: 120bpm (live performance in absolute time)</li> <li>Variational chorus detuning (vintage piano sound)</li> <li>Paired audio and MIDI data</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. The audio recordings were sonified from MIDI files containing historical musical compositions believed to be in the public domain and copyright free.</p> <p>The RGTM-PNO 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-EDM-TR8">GitHub repository</a>.</p>
WaivOps HH-LFBB: Open Audio Resources for Machine Learning in Music
<p><strong>WaivOps HH-LFBB Dataset</strong></p> <p>HH-LFBB is an open audio dataset composed of a series of drum recordings in the style of lofi hip-hop music. The dataset contains 3332 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.</p> <p>Specifications</p> <ul> <li>3332 audio loops (19.3 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 60-96bpm</li> <li>Expressive drum swings</li> <li>Lofi and boom bap style rhythms</li> </ul> <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 HH-LFBB 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-HH-LFBB">GitHub repository</a>.</p>
WaivOps RTRO-DRM: Open Audio Resources for Machine Learning in Music
<p><strong>WaivOps RTRO-DRM Dataset</strong></p> <p>RTRO-DRM is an open audio dataset composed of a series of drum recordings in the style of 1980s electronic music. The dataset comprises 2138 raw, unedited audio clips recorded in uncompressed stereo WAV format. These recordings were curated using an internal drum sample dataset and MIDI files sourced from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files. The files primarily consist of recordings that may not meet conventional audio quality standards but can still be valuable for a range of applications and research projects.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.</p> <p>Specifications</p> <ul> <li>2138 audio loops (4.3 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 100-145bpm</li> <li>Variational drum patterns</li> <li>Electronic drum machine sound (circa 1980s)</li> </ul> <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 RTRO-DRM 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-RTRO-DRM">GitHub repository</a>.</p>
WaivOps WRLD-LP: Open Audio Resources for Machine Learning in Music
<p><strong>WRLD-LP Dataset</strong></p> <p>WRLD-LP is an open audio dataset comprised of a series of symbolic drum recordings in the genres of world percussion music. The dataset includes 3,162 audio loops recorded in uncompressed stereo WAV format. The compositions were generated with an internal sample dataset played with note-dense MIDI drum files from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.</p> <p><strong>Specifications</strong></p> <ul> <li>3,162 audio loops (7.3 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 100-130bpm</li> <li>Expressive percussion drumming</li> <li>Mixed rhythms of world music</li> </ul> <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-LP 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-LP">GitHub repository</a>.</p>
Competition for resources can promote the divergence of social learning phenotypes
<p>Social learning occurs when animals acquire knowledge or skills by observing or interacting with others, and is the fundamental building block of culture. Within populations, some individuals use social learning more frequently than others, but why social learning phenotypes differ among individuals is poorly understood. We modelled the evolution of social learning frequency in a system where foragers compete for resources and there are many different foraging options to learn about. Social learning phenotypes diverged when some options offered much better rewards than others and expected rewards changed moderately quickly over time. When options offered similar rewards or when rewards changed slowly, a single social learning phenotype evolved. This held for fixed and simple conditional social learning rules. Sufficiently complex conditional social learning rules prevented the divergence of social learning phenotypes under all conditions. Our results explain how competition can promote the divergence of social learning phenotypes.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 2)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) only on the give ENGLISH corpus. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.