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41 results for “Musical Instruments”
Medley-solos-DB: a cross-collection dataset for musical instrument recognition
<p>Medley-solos-DB<br> =============<br> Version 1.2 March 2019.<br> </p> <p> </p> <p>Created By<br> --------------</p> <p>Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4).<br> <br> (1): New York University<br> (2): UC Berkeley<br> (3): Spotify, Inc.<br> (4): Télécom ParisTech</p> <p> </p> <p><br> Description<br> ---------------</p> <p> </p> <p>Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin.</p> <p>The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019).</p> <p> </p> <p>[1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>[2] J. Andén, V. Lostanlen, and S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing, vol. 67, no. 14, pp. 3704-3718, 2019. doi: 10.1109/TSP.2019.2918992</p> <p> </p> <p><br> Data Files<br> --------------</p> <p>The Medley-solos-DB contains 21571 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples.</p> <p>Every audio file has a name of the form:</p> <p>Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav</p> <p> </p> <p>For example:</p> <p>Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav</p> <p>corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set.</p> <p> </p> <p><br> Metadata Files<br> -------------------</p> <p>The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns:</p> <p>1. subset: either "training", "validation", or "test"</p> <p>2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc.</p> <p>3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience.</p> <p>4. song id: integer from 0 to 226. The track and artist names are anonymized.</p> <p>5. UUID4: universally unique identifier. Assigned and random, and different for every row.</p> <p> </p> <p>The list of instrument classes is:</p> <p>0. clarinet</p> <p>1. distorted electric guitar</p> <p>2. female singer</p> <p>3. flute</p> <p>4. piano</p> <p>5. tenor saxophone</p> <p>6. trumpet</p> <p>7. violin</p> <p> </p> <p><br> Please acknowledge Medley-solos-DB in academic research<br> ---------------------------------------------------------------------------------</p> <p>When Medley-solos-DB is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016.</p> <p>The creation of this dataset was supported by ERC InvariantClass grant 320959.</p> <p> </p> <p><br> Conditions of Use<br> ------------------------</p> <p>Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db.</p> <p>The Medley-solos-DB dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</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, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the Medley-solos-DB dataset or any part of it.</p> <p> </p> <p><br> Feedback<br> -------------</p> <p>Please help us improve Medley-solos-DB by sending your feedback to:<br> vincent.lostanlen@nyu.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p> </p> <p>Acknowledgement<br> -------------------------<br> We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.</p>
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
<p>The <em><strong>MAD-EEG Dataset</strong></em> is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. </p> <p>The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. </p> <p>For further details, please refer to the paper: <em><a href="https://hal.archives-ouvertes.fr/hal-02291882/document">MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</a>.</em></p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@inproceedings{Cantisani2019, author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard}, title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}}, year=2019, booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019}, pages={51--55}, doi={10.21437/SMM.2019-11}, url={http://dx.doi.org/10.21437/SMM.2019-11} }</code></pre> <p> </p>
The User Interface and Functionality Charts of Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This spreadsheet includes data related to user interface and functionality charts of Erkki Kurenniemi's electronic musical instruments. Data covers only musical instruments; not studio equipment. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. The data is visualized with a video published in https://vimeo.com/375784663</p> <p>PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p> <p>The outlining of charts is based on previous research on DMIs, e.g. by</p> <p>Birnbaum, D., Fiebrink, R., Malloch, J., & Wanderley, M. M. Towards a dimension space for musical devices. <em>Proceedings of the 2005 Conference on New Interfaces for Musical Expression, </em>192-195.</p> <p>Magnusson, T. An Epistemic Dimension Space for Musical Devices. <em>Proceedings of the 2010 Conference on New Interfaces for Musical Expression, </em>43-46.</p> <p>Wanderley, Mortensen M. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI.<em> Computer Music Journal, </em><em>26</em>(3), 62-76.</p>
Analyses of the works of art and events related to Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This speadsheet includes data related to analyses of works of art and events related to the Erkki Kurenniemi's electronic musical instruments. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by Mikko Ojanen.</p>
Appearances of Erkki Kurenniemi's Electronic Musical Instruments
<p>The excel spreadsheet includes notes about the appearances of the electronic musical instruments designed by the Finnish electroacoustic music pioneer Erkki Kurenniemi. The spreadsheet will be updated regularly when new information is found and checked. PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
Irish Traditional Music Instruments Thesaurus, Extended Version
<p>A Simple Knowledge Organisation System (SKOS) thesaurus. Incorporates extended instruments used in contemporary Irish traditional music. Developed for use at the Irish Traditional Music Archive. Contains Irish language and English terms.</p>
Irish Traditional Music Instruments Thesaurus, Core Version
<p>A Simple Knowledge Organisation System (SKOS) thesaurus. Incorporates core instruments used in contemporary Irish traditional music. Developed for use at the Irish Traditional Music Archive. Contains Irish language and English terms.</p>
Musical instrument playing with the Parametric hand data
<p>Data collected and source files for generation. Instructions on use and links to resources in README.txt</p> <p>Robot trajectories, logs, waveforms and loadcell data for experiments in embodied intelligence during music playing with the Parametric robot hand.</p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
"Dombo", a musical instrument
ID no.: MOK – M.O./A/7(a-c) Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1896 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
"Sanza", a musical instrument
ID no.: MOK – M.O./A/402 Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1922 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Slow practice and tempo management strategies in instrumental music learning: Investigating prevalence and cognitive functions
<p>This dataset corresponds to the publication of the same title and the following DOI: <a href="https://doi.org/10.1177%2F03057356211073481">https://doi.org/10.1177/03057356211073481</a></p> <p>The dataset contains 3 excel files. The file named QuantiativeSurveyDataClean_withKey contains all collected, unprocessed, cleaned data variables from the quantitative questionnaire. This includes all variables used for the principle components analysis. The file named ANOVA_data contains the variables used for the ANOVA analyses, and the file named regression_data contains the variables used for the regressions analyses. In all three files, descriptions of the variables can be found in the sheet titled "key", and the data can be found in the sheet titled "data".</p>
Music tracks from Lakh with different instrument bodies
<p>The current demo of the dataset contains 10 audio multi-tracks generated with three different sound bodies from the Lakh MIDIs (https://colinraffel.com/projects/lmd/) . The full version contains 3599 tracks.</p> <table> <tbody> <tr> <td>E1.tar.gz</td> <td>The first corpus of 10 multi-tracks</td> </tr> <tr> <td>E2.tar.gz</td> <td>The second corpus of 10 multi-tracks</td> </tr> <tr> <td>E3.tar.gz</td> <td>The third corpus of 10 multi-tracks</td> </tr> <tr> <td>mix1.flac</td> <td>Track 1 from E1 (example)</td> </tr> <tr> <td>mix2.flac</td> <td>Track 1 from E2 (example)</td> </tr> <tr> <td>mix3.flac</td> <td>Track 1 from E3 (example)</td> </tr> </tbody> </table> <p> </p>
Exploring the relation between fundamental frequency and spectral envelope in the perception of musical instrument sounds – sound files and participant responses
<p>This database contains synthesized instrument sounds (sounds.zip), participant responses (data.zip), and a key for the stimulus order (stimKey.zip) as complementary data to [1].</p> <p>Sounds include individual stimuli for two experiments. Experiment 1 contains stimuli used for sound pleasantness and sound brightness ratings. Every rating scale includes three acoustic conditions: congruent, incongruent, and fixed, corresponding to the relation of fundamental frequency (F0) and spectral envelope (SE). See [1] for further details on this matter. Experiment 2 contains stimuli for four synthesized instrument sounds: violin, alto voice, clarinet, and tuba. Sounds were synthesized using congruent spectral envelopes (all), and register fixed spectral envelopes (low, mid, high).</p> <p>All sounds are mono signals with a sampling frequency of 44100 Hz in WAV format.</p> <p>Data includes four sets of participant response data: sound pleasantness ratings (Exp. 1), sound brightness ratings (Exp. 1), sound pleasantness ratings (Exp. 2), and sound plausibility ratings (Exp. 2).</p> <p>StimKey provides values for the first and second principal components of the synthesis space for the pleasantness (1 to 81) and brightness (1 to 16) stimulus numbers for Exp. 1.</p> <p> </p> <p>Names convention for Exp. 2 sounds:</p> <p>InstrumentName_congruencyCondition_F0..wav</p> <p> </p> <p>References:</p> <p>[1] Jacobsen, S. and Siedenburg, K. (2024). Exploring the relation between fundamental frequency and spectral envelope in the perception of musical instrument sounds. Acta Acustica, 8, 48. <a href="https://doi.org/10.1051/aacus/2024038">https://doi.org/10.1051/aacus/2024038</a>.</p>
Raw data and stimuli for assessing the perceived reverberation in different rooms for a set of musical instrument sounds
<p>This set of data and sound stimuli was used in the study by Osses, McLachlan, and Kohlrausch (2020) to assess the perceived reverberation --including measurements and simulations-- for different instrument sounds in eight different rooms. The following are the directories that are provided:</p> <ul> <li><strong>00-Experiment-WAE_GM_201712</strong>: Web Audio Evaluation tool (WAE) used to run the listening experiment with 24 participants. Follow the instructions in README.txt to get the experiment running.</li> <li><strong>01-Stimuli</strong>: Sound stimuli as exactly used during the listening experiments.</li> <li><strong>02-Raw-data</strong> and <strong>03-Results-summary</strong>: Outputs from WAE for each of the participants. The raw data contained in these XML files were extracted and stored in '03-Results-summary'</li> <li><strong>04-Stimuli-9s-for-simulations</strong>: Same sounds as in '01-Stimuli' but truncated to have a duration of 9 s. These sounds were used as input to an implementation (Osses et al. 2017, 2020) of the model by van Dorp et al. (2013).</li> </ul> <p>The paper figures can be reproduced in two MATLAB toolboxes: fastACI (script: publ_osses2020a_JASA_EL_figs.m) and AMT (script: exp_osses2020.m, availability as of 2023).</p>
"Balafon", a musical instrument
ID no.: MOK – M.O./A/20(a-c) Museum: African Museum in Olkusz https://muzea.malopolska.pl/en/objects-list/1908 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Voices, instruments and somewhere in between: using musical medium to cross the knowledge organization/music boundary
<p>This paper considers the boundary between the library and information science (LIS) specialist and the music specialist's approach to music classification, and shows how crossing this boundary enhances knowledge on both sides. Musical medium – in other words, the forces needed to rehearse or perform a musical work – is at the heart of music classification. Therefore, three examples of music medium phenomena will be considered from a combined LIS/music perspective. </p><p>First, the "great" vocal/instrumental divide will be considered. The paper will explore how this fundamental musicological categorization is reflected in LIS classification schemes, and what happens when the categorization of a vocal/instrumental hybrid such as the choral symphony is attempted. The paper then considers the classification of musical instruments, comparing taxonomies within organology – the study of musical instruments – to LIS classification schemes for music. Part of this analysis will consider the categorization of instruments into "families", and how this differs between the music and LIS disciplines. This will be followed by an examination of the cross-fertilization between organology classification schemes and their bibliographic cousins, including an account of how the major organological taxonomy "Hornbostel and Sachs" crossed through the music boundary and into LIS territory. </p><p>Through exploring these three specific examples of issues in classifying music medium, the general relationship between the LIS and music disciplines can be examined. The paper will reveal that this relationship takes on many different guises, and that crossing the LIS/music boundary – in both directions – is critical to understanding the fundamental elements of music classification.</p>
Pipaset preview: A multimodal dataset for AMT and EA tasks dedicated to Chinese music instrument Pipa
<p>Yuancheng Wang, Yuyang Jing , Wei Wei, Dorian Cazau, Olivier Adam, Qiao Wang</p> <p>Accompanying <a href="http://github.com/yuanchengwang/TEAS">Website</a> here.</p> <p>If you make use of PipaSet for academic purposes, please cite the following publication:</p> <blockquote> <p>PipaSet and TEAS: A Multimodal Dataset and Annotation Platform for Automatic Music Transcription and Expressive Analysis dedicated to Chinese Traditional Plucked String Instrument Pipa. IEEE ACCESS 2022.</p> </blockquote> <p>This project was led by Yuancheng Wang at Information School of Information Science and Engineering, Southeast University, China, along with my supervisor Prof. Qiao Wang from same school and Dr. Yuyang Jing from Nanjing University of the Arts, Wei Wei form Xiaozhuang University, Dr. Dorian Cazau from Institute of Mines-Télécom Atlantique in Brest France, Prof. Olivier Adam from Sorbonne University.</p> <p>We present PipaSet, a dataset that provides multimodal pipa recordings alongside a high diversity of annotations for Automatic Music Transcription and Expressive Analysis tasks, including note, pitch contours, string and fret positions, and playing techniques. </p> <p>More information will be coming soon to cover more pieces of music played by pipa. </p>
IRMAS: a dataset for instrument recognition in musical audio signals
<p>This dataset includes musical audio excerpts with annotations of the predominant instrument(s) present. It was used for the evaluation in the following article:</p> <blockquote> <p>Bosch, J. J., Janer, J., Fuhrmann, F., & Herrera, P. “<a href="http://ismir2012.ismir.net/event/papers/559_ISMIR_2012.pdf">A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals</a>”, in Proc. ISMIR (pp. 559-564), 2012</p> </blockquote> <p>Please Acknowledge IRMAS in Academic Research</p> <p>IRMAS is intended to be used for training and testing methods for the automatic recognition of predominant instruments in musical audio. The instruments considered are: cello, clarinet, flute, acoustic guitar, electric guitar, organ, piano, saxophone, trumpet, violin, and human singing voice. This dataset is derived from the one compiled by Ferdinand Fuhrmann in his <a href="http://www.dtic.upf.edu/~ffuhrmann/PhD/">PhD thesis</a>, with the difference that we provide audio data in stereo format, the annotations in the testing dataset are limited to specific pitched instruments, and there is a different amount and lenght of excerpts.</p> <p><strong>Using this dataset</strong></p> <p>When IRMAS is used for academic research, we would highly appreciate if scientific publications of works partly based on the IRMAS dataset quote the above publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/irmas">https://www.upf.edu/web/mtg/irmas </a></p>
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