{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:12:08Z","timestamp":1740147128653,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Alma Mater Studiorum - Universit\u00e0 di Bologna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Adv Data Anal Classif"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, we review the main signal processing tools of Music Information Retrieval (MIR) from audio data, and we apply them to two recordings (by Leslie Howard and Thomas Rajna) of Franz Liszt\u2019s \u00c9tude S.136 no.1, with the aim of uncovering the macro-formal structure and comparing the interpretative styles of the two performers. In particular, after a thorough spectrogram analysis, we perform a segmentation based on the degree of novelty, in the sense of spectral dissimilarity, calculated frame-by-frame via the cosine distance. We then compare the metrical, temporal and timbrical features of the two executions by MIR tools. Via this method, we are able to identify in a data-driven way the different moments of the piece according to their melodic and harmonic content, and to find out that Rajna\u2019s execution is faster and less various, in terms of intensity and timbre, than Howard\u2019s one. This enquiry represents a case study able to show the potentialities of MIR from audio data in supporting traditional music score analyses and in providing objective information for statistically founded musical execution analyses.<\/jats:p>","DOI":"10.1007\/s11634-024-00594-6","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T02:01:33Z","timestamp":1714096893000},"page":"797-822","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Liszt\u2019s \u00c9tude S.136 no.1: audio data analysis of two different piano recordings"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2403-6599","authenticated-orcid":false,"given":"Matteo","family":"Farn\u00e8","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"594_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSP.2018.2869928","volume":"36","author":"E Benetos","year":"2018","unstructured":"Benetos E, Dixon S, Duan Z, Ewert S (2018) Automatic music transcription: an overview. IEEE Signal Process Mag 36:20\u201330","journal-title":"IEEE Signal Process Mag"},{"key":"594_CR2","doi-asserted-by":"crossref","unstructured":"Brillinger DR (2001) Time series: data analysis and theory. SIAM","DOI":"10.1137\/1.9780898719246"},{"key":"594_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MSP.2018.2874719","volume":"36","author":"E Cano","year":"2018","unstructured":"Cano E, Fitzgerald D, Liutkus A, Plumbley M, St\u00f6ter F (2018) Musical source separation: an introduction. IEEE Signal Process Mag 36:31\u201340","journal-title":"IEEE Signal Process Mag"},{"key":"594_CR4","doi-asserted-by":"crossref","unstructured":"Cohn R (2012) Audacious Euphony: Chromatic Harmony and the Triad\u2019s Second Nature. OUP USA","DOI":"10.1093\/acprof:oso\/9780199772698.001.0001"},{"key":"594_CR5","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1080\/09298210701653344","volume":"36","author":"D Ellis","year":"2007","unstructured":"Ellis D (2007) Beat tracking by dynamic programming. J New Music Res 36:51\u201360","journal-title":"J New Music Res"},{"key":"594_CR6","doi-asserted-by":"crossref","unstructured":"Ellis D, Graham E (2007) Identifying cover songs\u2019 with chroma features and dynamic programming beat tracking. In: 2007 IEEE international conference on acoustics, speech and signal processing-ICASSP\u201907, vol 4, pp IV\u20131429. IEEE","DOI":"10.1109\/ICASSP.2007.367348"},{"key":"594_CR7","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781316339633","volume-title":"Expanding the horizon of electroacoustic music analysis","author":"S Emmerson","year":"2016","unstructured":"Emmerson S, Landy L (2016) Expanding the horizon of electroacoustic music analysis. Cambridge University Press, Cambridge"},{"key":"594_CR8","volume-title":"Music and mathematics: from Pythagoras to fractals","author":"J Fauvel","year":"2006","unstructured":"Fauvel J, Flood R, Wilson R (2006) Music and mathematics: from Pythagoras to fractals. Oxford University Press, Oxford"},{"key":"594_CR9","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1007\/s11192-019-03166-0","volume":"120","author":"P Georges","year":"2019","unstructured":"Georges P, Nguyen N (2019) Visualizing music similarity: clustering and mapping 500 classical music composers. Scientometrics 120:975\u20131003","journal-title":"Scientometrics"},{"issue":"9","key":"594_CR10","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.3390\/electronics10091034","volume":"10","author":"K Gharaibeh","year":"2021","unstructured":"Gharaibeh K (2021) Assessment of various window functions in spectral identification of passive intermodulation. Electronics 10(9):1034","journal-title":"Electronics"},{"key":"594_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45728-3","volume-title":"Pattern detection and discovery","author":"D Hand","year":"2002","unstructured":"Hand D (2002) Pattern detection and discovery. Pattern detection and discovery. Springer, Berlin, pp 1\u201312"},{"key":"594_CR12","first-page":"673","volume":"10","author":"E Humphrey","year":"2015","unstructured":"Humphrey E, Bello J (2015) Four timely insights on automatic chord estimation. Proc ISMIR 10:673\u2013679","journal-title":"Proc ISMIR"},{"key":"594_CR13","unstructured":"Lartillot O, Cereghetti D, Eliard K, Grandjean D (2013) A simple, high-yield method for assessing structural novelty. In: The 3rd international conference on music and emotion, Jyv\u00e4skyl\u00e4, Finland, June 11\u201315. University of Jyv\u00e4skyl\u00e4, Department of Music, 2013"},{"key":"594_CR14","first-page":"937","volume":"86","author":"Y Kim","year":"2010","unstructured":"Kim Y, Schmidt E, Migneco R, Morton B, Richardson P, Scott J, Speck J, Turnbull D (2010) Music emotion recognition: a state of the art review. Proc ISMIR 86:937\u2013952","journal-title":"Proc ISMIR"},{"key":"594_CR15","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/978-3-540-78246-9_31","volume-title":"Data analysis, machine learning and applications","author":"O Lartillot","year":"2008","unstructured":"Lartillot O, Toiviainen P, Eerola T (2008) Mirtoolbox manual A Matlab toolbox for music information retrieval. Data analysis, machine learning and applications. Springer, Berlin, pp 261\u2013268"},{"key":"594_CR16","doi-asserted-by":"publisher","first-page":"5121","DOI":"10.3390\/app9235121","volume":"9","author":"O Lartillot","year":"2019","unstructured":"Lartillot O, Grandjean D (2019) Tempo and metrical analysis by tracking multiple metrical levels using autocorrelation. Appl Sci 9:5121","journal-title":"Appl Sci"},{"key":"594_CR17","doi-asserted-by":"publisher","DOI":"10.1002\/9781118393550","volume-title":"An introduction to audio content analysis: applications in signal processing and music informatics","author":"A Lerch","year":"2012","unstructured":"Lerch A (2012) An introduction to audio content analysis: applications in signal processing and music informatics. Wiley-IEEE Press, New Jersey"},{"key":"594_CR18","unstructured":"Lerch A, Arthur C, Pati A, Gururani S (2021) An interdisciplinary review of music performance analysis. ArXiv Preprint arXiv:2104.09018"},{"issue":"5","key":"594_CR19","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/TMM.2014.2325693","volume":"16","author":"T Li","year":"2014","unstructured":"Li T, Ogihara M, Tzanetakis G (2014) Guest editorial: special section on music data mining. IEEE Trans Multimed 16(5):1185\u20131187","journal-title":"IEEE Trans Multimed"},{"key":"594_CR20","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198714934.001.0001","volume-title":"Machine learning for signal processing: data science, algorithms, and computational statistics","author":"M Little","year":"2019","unstructured":"Little M (2019) Machine learning for signal processing: data science, algorithms, and computational statistics. Oxford University Press, Oxford"},{"key":"594_CR21","unstructured":"Martineau J (2008) The Elements of Music: Melody, Rhythm, and Harmony. Bloomsbury Publishing USA, 2008"},{"key":"594_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25931-4","volume-title":"Computational music analysis","author":"D Meredith","year":"2016","unstructured":"Meredith D (2016) Computational music analysis. Springer, Berlin"},{"key":"594_CR23","unstructured":"Moffat D, Ronan D, Reiss J (2015) An evaluation of audio feature extraction toolboxes"},{"key":"594_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21945-5","volume-title":"Fundamentals of music processing: audio, analysis, algorithms, applications","author":"M M\u00fcller","year":"2015","unstructured":"M\u00fcller M (2015) Fundamentals of music processing: audio, analysis, algorithms, applications. Springer, Berlin"},{"issue":"6","key":"594_CR25","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/JSTSP.2011.2112333","volume":"5","author":"M M\u00fcller","year":"2011","unstructured":"M\u00fcller M, Ellis P, Klapuri A, Richard G (2011) Signal processing for music analysis. IEEE J Select Top Signal Process 5(6):1088\u20131110","journal-title":"IEEE J Select Top Signal Process"},{"key":"594_CR26","doi-asserted-by":"publisher","first-page":"15993","DOI":"10.1038\/s41598-019-52380-6","volume":"9","author":"E Nakamura","year":"2019","unstructured":"Nakamura E, Kaneko K (2019) Statistical evolutionary laws in music styles. Sci Rep 9:15993","journal-title":"Sci Rep"},{"key":"594_CR27","unstructured":"Pauwels J, O\u2019Hanlon K, G\u00f3mez E, Sandler M et al (2019) 20 years of automatic chord recognition from audio. In: Proceedings of the ISMIR"},{"key":"594_CR28","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511622762","volume-title":"Spectral analysis for physical applications","author":"DB Percival","year":"1993","unstructured":"Percival DB, Walden AT (1993) Spectral analysis for physical applications. Cambridge University Press, Cambridge"},{"key":"594_CR29","volume-title":"Spectral analysis and time series","author":"MB Priestley","year":"1981","unstructured":"Priestley MB (1981) Spectral analysis and time series. Academic Press, Cambridge"},{"key":"594_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-11674-2","volume-title":"Advances in music information retrieval","author":"ZW Ras","year":"2010","unstructured":"Ras ZW, Wieczorkowska A (2010) Advances in music information retrieval, vol 274. Springer, Berlin"},{"issue":"1","key":"594_CR31","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.dsp.2007.12.004","volume":"19","author":"E Sejdi\u0107","year":"2009","unstructured":"Sejdi\u0107 E, Djurovi\u0107 I, Jiang J (2009) Time-frequency feature representation using energy concentration: an overview of recent advances. Digit Signal Proc 19(1):153\u2013183","journal-title":"Digit Signal Proc"},{"key":"594_CR32","volume-title":"Spectral analysis of signals","author":"P Stoica","year":"2005","unstructured":"Stoica P, Moses RL et al (2005) Spectral analysis of signals. Prentice-Hall, Upper Saddle River"},{"key":"594_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-9965-7","volume-title":"Fast Fourier transform algorithms for parallel computers","author":"D Takahashi","year":"2019","unstructured":"Takahashi D (2019) Fast Fourier transform algorithms for parallel computers. Springer, Berlin"},{"key":"594_CR34","first-page":"1","volume":"6","author":"T Theodorou","year":"2014","unstructured":"Theodorou T, Mporas I, Fakotakis N (2014) An overview of automatic audio segmentation. Int J Inf Technol Comput Sci (IJITCS) 6:1","journal-title":"Int J Inf Technol Comput Sci (IJITCS)"},{"key":"594_CR35","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TSA.2002.800560","volume":"10","author":"G Tzanetakis","year":"2002","unstructured":"Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10:293\u2013302","journal-title":"IEEE Trans Speech Audio Process"},{"issue":"6","key":"594_CR36","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/JSTSP.2011.2145356","volume":"5","author":"RJ Weiss","year":"2011","unstructured":"Weiss RJ, Bello JP (2011) Unsupervised discovery of temporal structure in music. IEEE J Select Top Signal Process 5(6):1240\u20131251","journal-title":"IEEE J Select Top Signal Process"},{"key":"594_CR37","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1177\/1029864918757595","volume":"23","author":"C Weiss","year":"2019","unstructured":"Weiss C, Mauch M, Dixon S, M\u00fcller M (2019) Investigating style evolution of Western classical music: a computational approach. Musicae Scientiae 23:486\u2013507","journal-title":"Musicae Scientiae"},{"key":"594_CR38","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s00530-017-0559-4","volume":"24","author":"X Yang","year":"2018","unstructured":"Yang X, Dong Y, Li J (2018) Review of data features-based music emotion recognition methods. Multimed Syst 24:365\u2013389","journal-title":"Multimed Syst"}],"container-title":["Advances in Data Analysis and Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-024-00594-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11634-024-00594-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-024-00594-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T03:41:29Z","timestamp":1726717289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11634-024-00594-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["594"],"URL":"https:\/\/doi.org\/10.1007\/s11634-024-00594-6","relation":{},"ISSN":["1862-5347","1862-5355"],"issn-type":[{"type":"print","value":"1862-5347"},{"type":"electronic","value":"1862-5355"}],"subject":[],"published":{"date-parts":[[2024,4,26]]},"assertion":[{"value":"2 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No funding was received for this research, and no Conflict of interest has to be declared. The musical traces used are copyrighted, so they cannot be made publicly available. The Matlab MIR toolbox (Lartillot et\u00a0al. ), on which this analysis is based, is freely accessible.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}