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76 results for “piano”
PiJAMA: Piano Jazz with Automatic MIDI Annotations
<p>Release of the automatic MIDI transcriptions that constitute the PiJAMA dataset. See below for the abstract of the publication.<br> <br> Abstract:</p> <p>Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present <strong>PiJAMA</strong> (<strong>Pi</strong>ano <strong>J</strong>azz with <strong>A</strong>utomatic <strong>M</strong>IDI <strong>A</strong>nnotations): a dataset of over 200 hours of solo jazz piano performances with automatically transcribed MIDI. In total there are 2,777 unique performances by 120 different pianists across 244 recorded albums. The dataset contains a mixture of studio recordings and live performances. We use automatic audio tagging to identify applause, spoken introductions, and other non-piano audio to facilitate downstream music information retrieval tasks. We explore descriptive statistics of the MIDI data, including pitch histograms and chromaticism. We then demonstrate two experimental benchmarks on the data: performer identification and generative modeling. The dataset, including a link to the associated source code is available at <a href="https://almostimplemented.github.io/PiJAMA/">https://almostimplemented.github.io/PiJAMA/</a>.</p>
Data and code from: Motor origins of timbre in piano performance
Open the record for dataset details and reuse information.
Ludwig van Beethoven – Piano Sonatas (A corpus of annotated scores)
No description provided.
1900s Piano Scene
1900s piano scene inspired by the 2016 film, The Light Between Oceans. This game-ready prop set comes complete with a low poly closed upright piano, kerosene lamp and piano stool. These props were modelled and UV edited in Maya. The texture art was made using GIMP-2.10. (I gotta admit I didn't choose to make this piano without reason. This damn movie lays waste to these heartstrings like nothing and sits comfortably in my "rainy-day rewatch collection".) Source: Objaverse 1.0 / Sketchfab
Caroline Harrison Piano
This object was 3D scanned with a [Creaform Go Scan 50.](https://www.onlineresourcesinc.com/) For more information about this item visit: https://bhpsite.org/ This object was 3D scanned by: [Connections XR](https://www.connectionsxr.com/) Source: Objaverse 1.0 / Sketchfab
Street piano
Jerry Silverberg, 2013 Bloor st, Toronto https://goo.gl/WlTGm1 I love it when street art takes advantage of the urban landscape like that. Source: Objaverse 1.0 / Sketchfab
Piano Raft
Photogrammetry test using mobile phone. Piano Raft been built over the 9 years and it travels the canals at the North of England. Source: Objaverse 1.0 / Sketchfab
BPSD: A Coherent Multi-Version Dataset for Analyzing the First Movements of Beethoven's Piano Sonatas
<p>-- Full paper: <strong><a href="https://doi.org/10.5334/tismir.196" target="_blank" rel="noopener noreferrer">https://doi.org/10.5334/tismir.196</a></strong> --</p> <p>This repository contains the Beethoven Piano Sonata Dataset (BPSD), a multi-version dataset focusing on the first movements of Beethoven's 32 piano sonatas. Recognized as pivotal works in classical music, Beethoven's piano sonatas have profoundly shaped Western classical music, holding a significant place in cultural history.<br><br>The BPSD includes sheet music in different machine-readable formats and audio recordings from eleven performances, with four of them being in the public domain and freely accessible for research purposes. A key feature of BPSD is its coherence, ensuring alignment of all versions on a unified musical timeline and enforcing consistent structures through careful editing of both score and audio representations.<br><br>The focus and main motivation for the design choices made in BPSD are on the technical and computational level. In particular, BPSD facilitates the assessment of algorithmic approaches in tasks like harmony analysis, structure analysis, music transcription, beat and downbeat estimation, and score following. The dataset's coherence makes it an ideal platform for systematically training and evaluating deep learning methods, shedding light on their robustness and uncovering data biases across different data splits using cross-version strategies for evaluation. <br><br>To ease applicability for computational approaches, the BPSD is based on various simplifications that may be disputable from a musicological perspective. Rather than providing novel musicological annotations, the main conceptual contribution of BPSD with its measure annotations is to provide a framework for transferring existing annotations from the symbolic to the audio domain. We hope that, as such, BPSD is also useful for the systematic analysis and exploration of Beethoven's piano sonatas, providing insights into their influence on the development of harmony and structure in Western classical music. Beyond research applications, the dataset also holds educational potential, aiding in the preparation and presentation of Beethoven's work to a broader audience through interactive multimedia experiences.</p>
PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance
<p>Recently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require more extensive guidance in piano performance. In this work, we construct a piano-hand motion generation benchmark to guide hand movements and fingerings for piano playing. To this end, we collect an annotated dataset, PianoMotion10M, consisting of 116 hours of piano playing videos from a bird's-eye view with 10 million annotated hand poses. We also introduce a powerful baseline model that generates hand motions from piano audios through a position predictor and a position-guided gesture generator. Furthermore, a series of evaluation metrics are designed to assess the performance of the baseline model, including motion similarity, smoothness, positional accuracy of left and right hands, and overall fidelity of movement distribution. Despite that piano key presses with respect to music scores or audios are already accessible, PianoMotion10M aims to provide guidance on piano fingering for instruction purposes.</p>
Towards Musically Informed Evaluation of Piano Transcription Models
<p>We provide here the evaluation set employed in our experiments described in "Towards Musically Informed Evaluation of Piano Transcription Models", published in the Proceedings of the 25th International Society for Music Information Retrieval Conference (ISMIR), San Francisco, United States, 2024.</p> <p>In this work, we demonstrate musically informed piano transcription metrics using transcriptions derived from three state-of-the-art transcriptions ([1], [2], [3]). To this end, we create an evaluation set that includes (1) a subset of the original audio recordings from the MAESTRO dataset [1], (2) a re-recorded version that subset, and (3) a perturbed version of recordings from both (1) and (2). In this data repository, we provide components (2) and (3).</p> <p>[1] Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, and Douglas Eck, “Enabling factorized piano music modeling and generation with the MAESTRO dataset,” in International Conference on Learning Representations, 2019. </p> <p>[2] Qiuqiang Kong, Bochen Li, Xuchen Song, Yuan Wan, and Yuxan Wang, “High-resolution piano transcription with pedals by regressing onset and offset times,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 29, pp. 3707–3717, 2021. </p> <p>[3] Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, and Jesse Engel. “Sequence-to-sequence piano transcription with transformers,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021.</p>
Feldman_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Morton Feldman, Why Patterns? for flute, glockenspiel and piano (1978)
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Feldman_dataset_PoD </strong>contains eight files:</p> <ul> <li>Feldman_01_ReadMe.pdf</li> <li>Feldman_02_data.xlsx (processed data for this work)</li> <li>Feldman_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Feldman_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Feldman_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Feldman_04_audio.mp3 to display correctly]</li> <li>Feldman_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Feldman_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Feldman_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Field Report on 3D Audio Capture of Solo Piano for Classical Music Productions - Audio Files
<p>This online repository contains audio files related to research on the side surround loudspeakers on 3D audio recordings of solo piano in classical music productions, undertaken by Emre Ekici, Will Howie, and Toru Kamekawa at Tokyo University of the Arts, March 2023. Please find the guidelines for usage below: </p> <p>The archive (3DPIANO_TRACKS.zip) contains 23 channels of mono audio tracks for ITU 4+7+0 solo piano recording, played by Yamaha Disklavier. Files are recorded at 96 kHz / 24-bit.</p> <p>File naming convention:<br> ##_ProjectTitle_Loudspeaker_Position/Variable_Position/Variable_Option (if applicable).</p> <p>Anyone is free to download and listen to these files for reference.</p> <p>If you wish to use these files for your research, please contact Emre Ekici (mrekici@outlook.com) or Will Howie (wghowie@gmail.com) for permission.</p>
Minecraft Piano
Piano I made in program named "Blockbench" Dowanload Link to The program: https://www.blockbench.net Source: Objaverse 1.0 / Sketchfab
The Effectiveness of Piano Therapy vs. Piano Listening on Manual Dexterity in the Elderly
ClinicalTrials.gov study NCT03372031. IPD Sharing: YES. Countries: 1. Publications: 4.
Stimuli and Results for "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription"
<p>This contains the stimuli and the participants data for the listening tests presented in the paper:</p> <p>Adrien Ycart, Lele Liu, Emmanouil Benetos, Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription". <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1):68-81, 2020 .</p> <p>More precisely, it contains:</p> <ul> <li>MAPS_midi_cut.zip: The MIDI files used to create the stimuli </li> <li>cut_points_seconds.zip: The points in seconds at which the MAPS music pieces were cut to make the stimuli. These correspond to manually-selected 5 to 10 seconds chunks, roughly corresponding to musical phrases.</li> <li>listening_test_results.zip: The data gathered during the listening test: <ul> <li>user_data.csv contains data about participants</li> <li>answers_data.csv contains the answers given by all participants</li> <li>comments.txt contains the comments left by the participants.</li> </ul> </li> </ul> <p>For any enquiries, please contact Adrien Ycart (a.ycart@qmul.ac.uk) or Emmanouil Benetos (emmanouil.benetos@qmul.ac.uk).</p> <p> </p>
Piano
Blue Marble Piano Texture: 2048x2048 Hope you like it : D Source: Objaverse 1.0 / Sketchfab
Piano Ukraine
Source: Objaverse 1.0 / Sketchfab
Piano
Source: Objaverse 1.0 / Sketchfab
THE INFLUENCE OF PITCH FEEDBACK ON LEARNING OF MOTOR-TIMING AND SEQUENCING: A PIANO STUDY WITH NOVICES
<p>These files contain values and conditions for each subject in csv format.</p>
Dataset for Analysing Effects of Piano Pedalling Techniques
<p>In this dataset, specifications with different conditions of pedalling techniques and piano touch were encoded as MIDI files, which were used to render audio using a Yamaha Disklavier grand piano.</p> <p>MIDI specifications for rendering isolated notes played with pedalling techniques on the sustain pedal include:<br> - pedal timing: anticipatory, rhythmic, legato;<br> - pedal depth: 127 (full pedal), 96 (three-quarter pedal), 64 (half pedal);<br> - note velocity: 96 (forte), 84 (mezzo-forte), 49 (piano);<br> - pitch: MIDI note value in [28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104].</p> <p>The above specifications on note velocity and pitch were used to render normally played isolated notes. To estimate partial frequencies and train NMF template for specific piano transcription, we generated all the 88 notes played at the mezzo-forte velocity without any pedalling technique. </p> <p>Apart from isolated notes, repeated notes (same note played repeatedly with an accelerated speed) and trills (rapid alternation between two adjacent notes) at three note velocities played without or with three depths of the anticipatory pedal were generated. Similar specifications were used to render chords and arpeggios (a group of notes from a chord played one after the other in an ascending/descending order). However, specifications on pitch were different:<br> - for chords: major, minor, diminished and augmented triad chord with MIDI value of root note in [36, 48, 60, 72, 84, 96];<br> - for arpeggios: notes in C major chord going up two octaves, including four arpeggios that correspond to MIDI note value in [[36, 40, 43, 48, 52, 55, 60], [48, 52, 55, 60, 64, 67, 72], [60, 64, 67, 72, 76, 79, 84], [72, 76, 79, 84, 88, 91, 96]].</p> <p>The same Disklavier piano were also used to playback MIDI files of four well-known pieces, including the third movement of the Piano Sonata No. 17 (Op. 31 No. 2-3) composed by Beethoven in 1801-02, Etude Op. 10 No. 3 composed by Chopin in 1832, the Ballades Op. 10 No. 1 composed by Brahms in 1854, and Jeux d'eau composed by Ravel in 1901. The SMD dataset already had the MIDI files of these four pieces, which were performed by professional pianists on a Disklavier.</p> <p>The recording was carried out at the Yamaha recording studio in Milton Keynes, United Kingdom, in March 2017. The instrument was a Yamaha Disklavier grand piano which was tuned directly prior to the recording session. The audio of the above MIDI rendering were recorded at a sampling rate of 44.1 kHz and a resolution of 24 bits, using the spaced-pair stereo microphone technique. A pair of Earthworks QTC40 omnidirectional condenser microphones was positioned about 50 cm above the strings. The positions were kept constant during the recording. </p>
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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.