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263 results for “listening”
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
<p>Data Release and facsimile of the survey, presented in the submission 3238 to the CSCW 2021 conference.</p> <p> </p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening Test Results
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the results of the listening test.</p>
The Distant Listening Corpus
<p dir="auto">This is a README file for a data repository originating from the <a href="https://github.com/DCMLab/dcml_corpora">DCML corpus initiative</a> and serves as welcome page for both</p> <ul> <li>the GitHub repo <a href="https://github.com/DCMLab/distant_listening_corpus">https://github.com/DCMLab/distant_listening_corpus</a> and the corresponding</li> <li>documentation page <a href="https://dcmlab.github.io/distant_listening_corpus" rel="nofollow">https://dcmlab.github.io/distant_listening_corpus</a></li> </ul> <p dir="auto">For information on how to obtain and use the dataset, please refer to <a href="https://dcmlab.github.io/distant_listening_corpus/introduction" rel="nofollow">this documentation page</a>.</p> <ul> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#the-distant-listening-corpus-a-corpus-of-annotated-scores">The Distant Listening Corpus (A corpus of annotated scores)</a> <ul> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#getting-the-data">Getting the data</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#data-formats">Data Formats</a> <ul> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#opening-scores">Opening Scores</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#opening-tsv-files-in-a-spreadsheet">Opening TSV files in a spreadsheet</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#loading-tsv-files-in-python">Loading TSV files in Python</a></li> </ul> </li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#version-history">Version history</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#questions-suggestions-corrections-bug-reports">Questions, Suggestions, Corrections, Bug Reports</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#cite-as">Cite as</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#license">License</a></li> </ul> </li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#overview">Overview</a> <ul> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#abc">ABC</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#bach_en_fr_suites">bach_en_fr_suites</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#bach_solo">bach_solo</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#bartok_bagatelles">bartok_bagatelles</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#beethoven_piano_sonatas">beethoven_piano_sonatas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#c_schumann_lieder">c_schumann_lieder</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#chopin_mazurkas">chopin_mazurkas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#corelli">corelli</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#couperin_clavecin">couperin_clavecin</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#couperin_concerts">couperin_concerts</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#cpe_bach_keyboard">cpe_bach_keyboard</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#debussy_suite_bergamasque">debussy_suite_bergamasque</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#dvorak_silhouettes">dvorak_silhouettes</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#frescobaldi_fiori_musicali">frescobaldi_fiori_musicali</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#grieg_lyric_pieces">grieg_lyric_pieces</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#handel_keyboard">handel_keyboard</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#jc_bach_sonatas">jc_bach_sonatas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#kleine_geistliche_konzerte">kleine_geistliche_konzerte</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#kozeluh_sonatas">kozeluh_sonatas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#liszt_pelerinage">liszt_pelerinage</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#mahler_kindertotenlieder">mahler_kindertotenlieder</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#medtner_tales">medtner_tales</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#mendelssohn_quartets">mendelssohn_quartets</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#monteverdi_madrigals">monteverdi_madrigals</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#mozart_piano_sonatas">mozart_piano_sonatas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#pergolesi_stabat_mater">pergolesi_stabat_mater</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#peri_euridice">peri_euridice</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#pleyel_quartets">pleyel_quartets</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#poulenc_mouvements_perpetuels">poulenc_mouvements_perpetuels</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#rachmaninoff_piano">rachmaninoff_piano</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#ravel_piano">ravel_piano</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#scarlatti_sonatas">scarlatti_sonatas</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#schubert_winterreise">schubert_winterreise</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#schulhoff_suite_dansante_en_jazz">schulhoff_suite_dansante_en_jazz</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#schumann_kinderszenen">schumann_kinderszenen</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#schumann_liederkreis">schumann_liederkreis</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#sweelinck_keyboard">sweelinck_keyboard</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#tchaikovsky_seasons">tchaikovsky_seasons</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#wagner_overtures">wagner_overtures</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/#wf_bach_sonatas">wf_bach_sonatas</a></li> </ul> </li> </ul> <div dir="auto"> <h1>The Distant Listening Corpus (A corpus of annotated scores)</h1> <a href="https://github.com/DCMLab/distant_listening_corpus/#the-distant-listening-corpus-a-corpus-of-annotated-scores"></a></div> <p dir="auto"><em>A modular infrastructure for the empirical study of (an)notated music</em></p> <p dir="auto">This corpus has been created within the <a href="https://github.com/DCMLab/dcml_corpora">DCML corpus initiative</a> and employs the <a href="https://github.com/DCMLab/standards">DCML harmony annotation standard</a>.</p> <p dir="auto">The publication covers the following public corpora (the DOI links always point at the latest version respectively):</p> <ul> <li>J.S. Bach – English and French Suites [<a href="https://doi.org/10.5281/zenodo.14996489" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/bach_en_fr_suites">repo</a>][<a href="https://github.com/DCMLab/bach_en_fr_suites/archive/refs/heads/main.zip">ZIP</a>]</li> <li>J.S. Bach – Solo Pieces (A corpus of annotated scores) [<a href="https://doi.org/10.5281/zenodo.14996765" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/bach_solo">repo</a>][<a href="https://github.com/DCMLab/bach_solo/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Béla Bartók – 14 Bagatelles, Op. 6 [<a href="https://doi.org/10.5281/zenodo.14996945" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/bartok_bagatelles">repo</a>][<a href="https://github.com/DCMLab/bartok_bagatelles/archive/refs/heads/main.zip">ZIP</a>]</li> <li>François Couperin – L'art de toucher le clavecin [<a href="https://doi.org/10.5281/zenodo.14984598" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/couperin_clavecin">repo</a>][<a href="https://github.com/DCMLab/couperin_clavecin/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Clara Schumann – Lieder [<a href="https://doi.org/10.5281/zenodo.14996952" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/c_schumann_lieder">repo</a>][<a href="https://github.com/DCMLab/c_schumann_lieder/archive/refs/heads/main.zip">ZIP</a>]</li> <li>François Couperin – Concerts Royaux [<a href="https://doi.org/10.5281/zenodo.15027239" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/couperin_concerts">repo</a>][<a href="https://github.com/DCMLab/couperin_concerts/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Carl Philipp Emanuel Bach – Works for Keyboard [<a href="https://doi.org/10.5281/zenodo.14996326" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/cpe_bach_keyboard">repo</a>][<a href="https://github.com/DCMLab/cpe_bach_keyboard/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Girolamo Frescobaldi (1583-1643) – Fiori Musicali, op. 12 (1635) [<a href="https://doi.org/10.5281/zenodo.14984864" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/frescobaldi_fiori_musicali">repo</a>][<a href="https://github.com/DCMLab/frescobaldi_fiori_musicali/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Georg Friedrich Händel – Grobschmied Variations (The Harmonious Blacksmith), HWV 430 [<a href="https://doi.org/10.5281/zenodo.14996996" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/handel_keyboard">repo</a>][<a href="https://github.com/DCMLab/handel_keyboard/archive/refs/heads/main.zip">ZIP</a>]</li> <li>J.C. Bach – Keyboard Sonatas [<a href="https://doi.org/10.5281/zenodo.14996292" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/jc_bach_sonatas">repo</a>][<a href="https://github.com/DCMLab/jc_bach_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Heinrich Schütz – Kleine Geistliche Konzerte [<a href="https://doi.org/10.5281/zenodo.14997003" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/kleine_geistliche_konzerte">repo</a>][<a href="https://github.com/DCMLab/kleine_geistliche_konzerte/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Leopold Koželuch – Piano Sonatas [<a href="https://doi.org/10.5281/zenodo.14997015" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/kozeluh_sonatas">repo</a>][<a href="https://github.com/DCMLab/kozeluh_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Gustav Mahler – Kindertotenlieder [<a href="https://doi.org/10.5281/zenodo.14997022" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/mahler_kindertotenlieder">repo</a>][<a href="https://github.com/DCMLab/mahler_kindertotenlieder/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Felix Mendelssohn – String Quartets [<a href="https://doi.org/10.5281/zenodo.14996150" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/mendelssohn_quartets">repo</a>][<a href="https://github.com/DCMLab/mendelssohn_quartets/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Claudio Monteverdi – Madrigals [<a href="https://doi.org/10.5281/zenodo.15003026" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/monteverdi_madrigals">repo</a>][<a href="https://github.com/DCMLab/monteverdi_madrigals/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Giovanni Battista Pergolesi – Stabat Mater (1736) [<a href="https://doi.org/10.5281/zenodo.14990099" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/pergolesi_stabat_mater">repo</a>][<a href="https://github.com/DCMLab/pergolesi_stabat_mater/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Jacopo Peri – Euridice (1600) [<a href="https://doi.org/10.5281/zenodo.14996445" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/peri_euridice">repo</a>][<a href="https://github.com/DCMLab/peri_euridice/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Ignaz Pleyel – String Quartets [<a href="https://doi.org/10.5281/zenodo.14997048" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/pleyel_quartets">repo</a>][<a href="https://github.com/DCMLab/pleyel_quartets/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Francis Poulenc – Mouvements Perpetuels [<a href="https://doi.org/10.5281/zenodo.14997053" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/poulenc_mouvements_perpetuels">repo</a>][<a href="https://github.com/DCMLab/poulenc_mouvements_perpetuels/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Sergei Rachmaninoff – Piano Pieces [<a href="https://doi.org/10.5281/zenodo.14984155" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/rachmaninoff_piano">repo</a>][<a href="https://github.com/DCMLab/rachmaninoff_piano/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Maurice Ravel – Piano Pieces [<a href="https://doi.org/10.5281/zenodo.14997064" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/ravel_piano">repo</a>][<a href="https://github.com/DCMLab/ravel_piano/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Domenico Scarlatti – Keyboard Sonatas [<a href="https://doi.org/10.5281/zenodo.14992884" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/scarlatti_sonatas">repo</a>][<a href="https://github.com/DCMLab/scarlatti_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Franz Schubert – Winterreise [<a href="https://doi.org/10.5281/zenodo.14997095" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/schubert_winterreise">repo</a>][<a href="https://github.com/DCMLab/schubert_winterreise/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Erwin Schulhoff – Suite dansante en jazz [<a href="https://doi.org/10.5281/zenodo.14997098" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/schulhoff_suite_dansante_en_jazz">repo</a>][<a href="https://github.com/DCMLab/schulhoff_suite_dansante_en_jazz/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Robert Schumann – Liederkreis [<a href="https://doi.org/10.5281/zenodo.14997104" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/schumann_liederkreis">repo</a>][<a href="https://github.com/DCMLab/schumann_liederkreis/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Jan Sweelinck – Organ Pieces [<a href="https://doi.org/10.5281/zenodo.14997111" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/sweelinck_keyboard">repo</a>][<a href="https://github.com/DCMLab/sweelinck_keyboard/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Richard Wagner – Overtures [<a href="https://doi.org/10.5281/zenodo.14997120" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/wagner_overtures">repo</a>][<a href="https://github.com/DCMLab/wagner_overtures/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Wilhelm Friedemann Bach – Piano Sonatas [<a href="https://doi.org/10.5281/zenodo.14997133" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/wf_bach_sonatas">repo</a>][<a href="https://github.com/DCMLab/wf_bach_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> </ul> <p dir="auto"><em>Hentschel, J., Rammos, Y., Neuwirth, M., Moss, F. C., & Rohrmeier, M. (2024). An annotated corpus of tonal piano music from the long 19th century. Empirical Musicology Review, 18(1), 84–95. <a href="https://doi.org/10.18061/emr.v18i1.8903" rel="nofollow">https://doi.org/10.18061/emr.v18i1.8903</a></em></p> <ul> <li>Ludwig van Beethoven - Piano Sonatas [<a href="https://doi.org/10.5281/zenodo.7473560" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/beethoven_piano_sonatas">repo</a>][<a href="https://github.com/DCMLab/beethoven_piano_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Frédéric Chopin - Mazurkas [<a href="https://doi.org/10.5281/zenodo.7473566" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/chopin_mazurkas">repo</a>][<a href="https://github.com/DCMLab/chopin_mazurkas/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Claude Debussy - Suite Bergamasque [<a href="https://doi.org/10.5281/zenodo.7473568" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/debussy_suite_bergamasque">repo</a>][<a href="https://github.com/DCMLab/debussy_suite_bergamasque/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Antonín Dvořák - Silhouettes [<a href="https://doi.org/10.5281/zenodo.7473576" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/dvorak_silhouettes">repo</a>][<a href="https://github.com/DCMLab/dvorak_silhouettes/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Edvard Grieg - Lyric Pieces [<a href="https://doi.org/10.5281/zenodo.7473578" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/grieg_lyric_pieces">repo</a>][<a href="https://github.com/DCMLab/grieg_lyric_pieces/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Franz Liszt - Années de Pèlerinage [<a href="https://doi.org/10.5281/zenodo.7473580" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/liszt_pelerinage">repo</a>][<a href="https://github.com/DCMLab/liszt_pelerinage/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Nikolai Medtner - Tales [<a href="https://doi.org/10.5281/zenodo.7473528" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/medtner_tales">repo</a>][<a href="https://github.com/DCMLab/medtner_tales/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Robert Schumann - Kinderszenen [<a href="https://doi.org/10.5281/zenodo.7473582" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/schumann_kinderszenen">repo</a>][<a href="https://github.com/DCMLab/schumann_kinderszenen/archive/refs/heads/main.zip">ZIP</a>]</li> <li>Pyotr Tchaikovsky - The Seasons [<a href="https://doi.org/10.5281/zenodo.7473586" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/tchaikovsky_seasons">repo</a>][<a href="https://github.com/DCMLab/tchaikovsky_seasons/archive/refs/heads/main.zip">ZIP</a>]</li> </ul> <p dir="auto"><em>Hentschel, J., Moss, F. C., Neuwirth, M., & Rohrmeier, M. A. (2021). A semi-automated workflow paradigm for the distributed creation and curation of expert annotations. Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR, 262–269. <a href="https://doi.org/10.5281/ZENODO.5624417" rel="nofollow">https://doi.org/10.5281/ZENODO.5624417</a></em></p> <ul> <li>Arcangelo Corelli – Trio Sonatas [<a href="https://zenodo.org/doi/10.5281/zenodo.7504011" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/corelli">repo</a>][<a href="https://github.com/DCMLab/corelli/archive/refs/heads/main.zip">ZIP</a>]</li> </ul> <p dir="auto"><em>Hentschel, J., Neuwirth, M., & Rohrmeier, M. (2021). The Annotated Mozart Sonatas: Score, harmony, and cadence. Transactions of the International Society for Music Information Retrieval, 4(1), 67–80. <a href="https://doi.org/10.5334/tismir.63" rel="nofollow">https://doi.org/10.5334/tismir.63</a></em></p> <ul> <li>Wolfgang Amadeus Mozart - Piano Sonatas [<a href="https://zenodo.org/doi/10.5281/zenodo.7424962" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/mozart_piano_sonatas">repo</a>][<a href="https://github.com/DCMLab/mozart_piano_sonatas/archive/refs/heads/main.zip">ZIP</a>]</li> </ul> <p dir="auto"><em>Neuwirth, M., Harasim, D., Moss, F. C., & Rohrmeier, M. (2018). The Annotated Beethoven Corpus (ABC): A Dataset of Harmonic Analyses of All Beethoven String Quartets. Frontiers in Digital Humanities, 5(July), 1–5. <a href="https://doi.org/10.3389/fdigh.2018.00016" rel="nofollow">https://doi.org/10.3389/fdigh.2018.00016</a></em></p> <ul> <li>Ludwig van Beethoven - String Quartets [<a href="https://zenodo.org/doi/10.5281/zenodo.7441343" rel="nofollow">DOI</a>][<a href="https://github.com/DCMLab/ABC">repo</a>][<a href="https://github.com/DCMLab/ABC/archive/refs/heads/main.zip">ZIP</a>]</li> </ul> <div dir="auto"> <h2>Getting the data</h2> <a href="https://github.com/DCMLab/distant_listening_corpus/#getting-the-data"></a></div> <ul> <li>download individual subcorpora as ZIP files using the URLs provided above</li> <li>download a <a href="https://specs.frictionlessdata.io/data-package/" rel="nofollow">Frictionless Datapackage</a> that includes concatenations of the TSV files in the four folders (<code>measures</code>, <code>notes</code>, <code>chords</code>, and <code>harmonies</code>) and a JSON descriptor: <ul> <li><a href="https://github.com/DCMLab/distant_listening_corpus/releases/latest/download/distant_listening_corpus.zip">distant_listening_corpus.zip</a></li> <li><a href="https://github.com/DCMLab/distant_listening_corpus/releases/latest/download/distant_listening_corpus.datapackage.json">distant_listening_corpus.datapackage.json</a></li> </ul> </li> <li>clone the repo (~2.4 GB): <code>git clone --recursive -j12 https://github.com/DCMLab/distant_listening_corpus.git</code></li> </ul> <div dir="auto"> <h2>Data Formats</h2> <a href="https://github.com/DCMLab/distant_listening_corpus/#data-formats"></a></div> <p dir="auto">Each piece in this corpus is represented by five files with identical name prefixes, each in its own folder. For example, the <em>Prélude</em> of J.S. Bach’s first English Suite, BWV 806, has the following files:</p> <ul> <li><code>MS3/BWV806_01_Prelude.mscx</code>: Uncompressed MuseScore 3.6.2 file including the music and annotation labels.</li> <li><code>notes/BWV806_01_Prelude.notes.tsv</code>: A table of all note heads contained in the score and their relevant features (not each of them represents an onset, some are tied together)</li> <li><code>measures/BWV806_01_Prelude.measures.tsv</code>: A table with relevant information about the measures in the score.</li> <li><code>chords/BWV806_01_Prelude.chords.tsv</code>: A table containing layer-wise unique onset positions with the musical markup (such as dynamics, articulation, lyrics, figured bass, etc.).</li> <li><code>harmonies/BWV806_01_Prelude.harmonies.tsv</code>: A table of the included harmony labels (including cadences and phrases) with their positions in the score.</li> </ul> <p dir="auto">Each TSV file comes with its own JSON descriptor that describes the meanings and datatypes of the columns ("fields") it contains, follows the <a href="https://specs.frictionlessdata.io/tabular-data-resource/" rel="nofollow">Frictionless specification</a>, and can be used to validate and correctly load the described file.</p> <div dir="auto"> <h3>Opening Scores</h3> <a href="https://github.com/DCMLab/distant_listening_corpus/#opening-scores"></a></div> <p dir="auto">After navigating to your local copy, you can open the scores in the folder <code>MS3</code> with the free and open source score editor <a href="https://musescore.org" rel="nofollow">MuseScore</a>. Please note that the scores have been edited, annotated and tested with <a href="https://github.com/musescore/MuseScore/releases/tag/v3.6.2">MuseScore 3.6.2</a>. MuseScore 4 has since been released which renders them correctly but cannot store them back in the same format.</p> <div dir="auto"> <h3>Opening TSV files in a spreadsheet</h3> <a href="https://github.com/DCMLab/distant_listening_corpus/#opening-tsv-files-in-a-spreadsheet"></a></div> <p dir="auto">Tab-separated value (TSV) files are like Comma-separated value (CSV) files and can be opened with most modern text editors. However, for correctly displaying the columns, you might want to use a spreadsheet or an addon for your favourite text editor. When you use a spreadsheet such as Excel, it might annoy you by interpreting fractions as dates. This can be circumvented by using <code>Data --> From Text/CSV</code> or the free alternative <a href="https://www.libreoffice.org/download/download/" rel="nofollow">LibreOffice Calc</a>. Other than that, TSV data can be loaded with every modern programming language.</p> <div dir="auto"> <h3>Loading TSV files in Python</h3> <a href="https://github.com/DCMLab/distant_listening_corpus/#loading-tsv-files-in-python"></a></div> <p dir="auto">Since the TSV files contain null values, lists, fractions, and numbers that are to be treated as strings, you may want to use this code to load any TSV files related to this repository (provided you're doing it in Python). After a quick <code>pip install -U ms3</code> (requires Python 3.10 or later) you'll be able to load any TSV like this:</p> <div dir="auto"> <pre><span>import</span> <span>ms3</span> <span>labels</span> <span>=</span> <span>ms3</span>.<span>load_tsv</span>(<span>"harmonies/BWV806_01_Prelude.harmonies.tsv"</span>) <span>notes</span> <span>=</span> <span>ms3</span>.<span>load_tsv</span>(<span>"notes/BWV806_01_Prelude.notes.tsv"</span>)</pre> <div> </div> </div> <div dir="auto"> <h2>Version history</h2> <a href="https://github.com/DCMLab/distant_listening_corpus/#version-history"></a></div> <p dir="auto">See the <a href="https://github.com/DCMLab/distant_listening_corpus/releases">GitHub releases</a>.</p> <div dir="auto"> <h2>Questions, Suggestions, Corrections, Bug Reports</h2> <a href="https://github.com/DCMLab/distant_listening_corpus/#questions-suggestions-corrections-bug-reports"></a></div> <p>Please <a href="https://github.com/DCMLab/distant_listening_corpus/issues">create an issue</a> and/or feel free to fork and submit pull requests.</p>
Data set of two dual-task paradigms to measure listening effort in cochlear implant users
<p>This data set presents the data from the paper by Hendrikse, Dingemanse, & Goedegebure (2022). This study aimed to investigate the feasibility of using listening effort to measure relatively small differences in SNR, as would arise from different hearing-device settings. Listening effort was chosen, because there are indications in literature that listening effort may be more sensitive to differences between hearing-device settings than established speech intelligibility measures. Two behavioral listening effort tests were performed at two signal-to-noise ratios (SNRs) where the intelligibility was high. A sentence final word identification and recall test (SWIRT), and a sentence verification test (SVT) were compared with a group of 18 Dutch CI users. SWIRT measured the ability to recall the final words of sentences after a list of five or seven sentences was presented. The SVT measured the ability and reaction time to determine whether a sentence was true or false. Both tests were conducted in background noise at SNRs +4 dB and +8 dB above the 50% speech perception threshold. The structure of the data files is explained in the README file.</p>
A dataset recording joint EEG-fMRI during affective music listening
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narrative listening
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Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
The role of place cues in voluntary stream segregation for cochlear implant listeners
<p>Data generated for the study "The role of place cues in voluntary stream segregation for cochlear implant listeners" - <a href="https://doi.org/10.1177/2331216517750262">https://doi.org/10.1177/2331216517750262</a></p> <p>The files "Experiment_1.txt" and "Experiment_2.txt" contain the data from the first and second experiments, respectively.</p> <p>List of variables:</p> <ul> <li>Subject: Listener's ID</li> <li>Electrode: Stimulation electrode for the distractor stream. The target stream was always presented on electrode 11.</li> <li>Rate: Stimulation pulse rate.</li> <li>ABpairs: Number of AB duplets in the sequence.</li> <li>Hrate: Hit rate</li> <li>FArate: False alarm rate</li> <li>dprime: d' score</li> <li>d_se: Standard error of the d' score</li> <li>IOmodel: 1 for ideal observer model estimates and 0 for listener's d' scores</li> </ul>
Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset
<p>This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>.</p> <p>Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>".</p> <p>The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: <a href="https://github.com/KarimMibrahim/Situational_Session_Generator.git">https://github.com/KarimMibrahim/Situational_Session_Generator.git</a></p> <p>The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a>. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. </p> <p>Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition. </p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
Listening experiment and Stimuli for: Adjustable Deterministic Pseudonymization of Speech
<p>Web pages for listening experiment, with stimuli included, as reported in: Adjustable Deterministic Pseudonymization of Speech</p> <p>The listening experiments can be run locally offline. After unpacking the files, the listening experiment can be run locally or in a web site by pointing a web browser to the index.html file.</p> <p>A report discussing the pseudonymization results can be found at doi: 10.5281/zenodo.3773931</p> <p>A dataset created with this expriment can be found at doi: 10.5281/zenodo.3773936</p> <p>The <em>akouste</em> listening experiment software can be found at doi: 10.5281/zenodo.3712142 on Github</p> <p>The <em>Pseudonymize</em> <em>Speech</em> <em>Praat</em> script can be found at doi: 10.5281/zenodo.3712140</p> <p>The <em>Praat</em> speech software can be found at <em>www.praat.org</em></p>
Binaural room impulse responses of a 5.0 surround setup for different listening positions
<p>Binaural Room Impulse Responses - KEMAR, room Calypso, TU Berlin, 5.0 Surround setup<br /> </p> <p>This dataset contains binaural room impulse responses (BRIRs) measured at nine<br /> different listening positions for a 5.0 surround setup in the listening room<br /> Calypso in the Telefunken building of Technische Universität Berlin, Berlin,<br /> Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s<br /> at a frequency of 1 kHz.</p> <p>"doc.zip" contains additional information for the measurement,<br /> "*.sofa" are the actual BRIRs, one file for every listening position, where the<br /> position is indicated by the X*-Y*-values.<br /> In order to work with those files you need a SOFA API for your programming<br /> language. For example, the one for Matlab can be found here:<br /> https://github.com/sofacoustics/API_MO/releases/latest<br /> If you want to create a single SOFA file containing all listening positions, you<br /> can execute the "combine_positions.m" script in Matlab after installing and<br /> starting the SOFA API.</p> <p>The BRIRs were with a head-orientation varying in the range of +-90° with 1°<br /> resolution. Room shape and size, listener and sound source positions are shown<br /> in "setup_calypso_surround_genelec8030A.pdf".</p> <p>Directory "./photos" contains photographs of the measurement setup.</p> <p>Copyright 2016 Hagen Wierstorf</p> <p>Licensed under Creative Commons (CC-BY-4.0)</p>
Binaural room impulse responses of a 5.0 surround setup for different listening positions
<p>This dataset contains binaural room impulse responses (BRIRs) measured at nine<br> different listening positions for a 5.0 surround setup in the listening room<br> Calypso in the Telefunken building of Technische Universität Berlin, Berlin,<br> Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s<br> at a frequency of 1 kHz. The measurment was done with Genelec 8030A loudspeakers<br> and repeated for the central listening positon with the larger Genelec 8250A<br> loudspeakers.</p> <p>"doc.zip" contains additional information for the measurement,<br> "*.sofa" are the actual BRIRs, one file for every listening position, where the<br> position is indicated by the X*-Y*-values. The file<br> `KEMAR_Calypso_Surround.sofa` contains all listening positions in one file.<br> In order to work with those files you need a SOFA API for your programming<br> language. For example, the one for Matlab can be found here:<br> https://github.com/sofacoustics/API_MO/releases/latest</p> <p>The BRIRs were with a head-orientation varying in the range of +-90° with 1°<br> resolution. Room shape and size, listener and sound source positions are shown<br> in `setup_calypso_surround_genelec8030A.pdf` and <br> `setup_calypso_surround_genelec8250A.pdf`.</p> <p>Directory "./photos" contains photographs of the measurement setup.</p> <p>Copyright 2016 Hagen Wierstorf</p> <p>Licensed under Creative Commons (CC-BY-4.0)<br> </p>
Listening preferences for variations of pop mixes in Wave Field Synthesis
<p>This data set includes results for the listening tests described in<br> http://doi.org/10.5281/zenodo.61000.</p> <p>See the file README.md for more details.</p>
Binaural room impulse responses: Same listener-source-setup at different positions in the room
<p>To study the perception of room acoustics in dependency of the position in the room, measurements with KEMAR head-and-torso-simulator were conducted. The dummy head was placed at 5 different positions in a small conference room (10.3mx5.8mx3.1m, RT=0.65s). The source, a loudspeaker Genelec 1030A, was always positioned in the same relation to the listening position. BRIRs were measured with an azimuth-resolution of 5° from 0°-360°. This data allows a psychoacoustical comparison of the room acoustical properties at different positions in the room.</p>
Binaural room impulse responses: Same listener-source-setup at different positions in the room
<p>To study the perception of room acoustics in dependency of the position in the room, measurements with KEMAR head-and-torso-simulator were conducted. The dummy head was placed at 5 different positions in a small conference room (10.3mx5.8mx3.1m, RT=0.65s). The source, a loudspeaker Genelec 1030A, was always positioned in the same relation to the listening position. BRIRs were measured with an azimuth-resolution of 5° from 0°-360°. This data allows a psychoacoustical comparison of the room acoustical properties at different positions in the room.</p>
Listening preferences for the different reproduction systems Stereo, Surround, and Wave Field Synthesis in the context of popular music
<p>We did a paired comparison preference test where listeners rated their listening preference for four different pop musical pieces presented by WFS, stereo or surround. The musical pieces were all mixed by the same person in order to try to minimize the influence of the mix on the ratings, but still trying to get the best out of every system, see [1] for details. The mixes are available at https://doi.org/10.14279/depositonce-5173.</p> <p>Here, we provide the results of the 22 listeners that participated in the experiment together with an analysis which calculates a Bradley-Terry-Luce model after Wickelmayer et al. [2].</p> <p>[1] Hold, C., Wierstorf, H., Raake, A. (2016), “The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music,” 140th AES Convention, Paper 9533</p> <p>[2] https://cran.r-project.org/web/packages/eba/index.html</p>
Listening position preference for different 5.0 reproductions -- data
<p>We performed an experiment which investigated the preferred listening position<br> in a 5.0 surround setup out of nine different positions while listening to a<br> classical piece of music.<br> The test was performed by binaural synthesis in order to allow instantaneous<br> switching between the different positions and was performed first without<br> indicating to the participants where they are located in the virtual setup and a<br> second run, where they got visual feedback on their virtual position.</p> <p>The experiment was repeated for six different recording techniques, that captured the Mozart performance simultaneously [2].</p> <p>See README.md for more details.</p> <p>[1] https://github.com/SoundScapeRenderer/ssr</p> <p>[2] Wittek, H. (2015), “ORF Surround sound techniques, 2002,”<br> http://www.hauptmikrofon.de/stereo-3d/orf-surround-techniques, last access:<br> 2016/10/21<br> </p>
ScienceDex guides
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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.