Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis
In the area of music information retrieval (MIR), automatic music transcription is considered one of the most challenging tasks, for which many different techniques have been proposed. This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset...
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| Veröffentlicht in: | IEEE transactions on audio, speech, and language processing speech, and language processing, 2011-08, Vol.19 (6), p.1610-1630 |
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| creator | Argenti, F Nesi, P Pantaleo, G |
| description | In the area of music information retrieval (MIR), automatic music transcription is considered one of the most challenging tasks, for which many different techniques have been proposed. This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset times, durations, and intensity of concurrent sounds in audio recordings, played by one or more instruments. Pitch estimation is carried out by means of a front-end that jointly uses a constant-Q and a bispectral analysis of the input audio signal; subsequently, the processed signal is correlated with a fixed 2-D harmonic pattern. Onsets and durations detection procedures are based on the combination of the constant-Q bispectral analysis with information from the signal spectrogram. The detection process is agnostic and it does not need to take into account musicological and instrumental models or other a priori knowledge. The system has been validated against the standard Real-World Computing (RWC)-Classical Audio Database. The proposed method has demonstrated good performances in the multiple F 0 tracking task, especially for piano-only automatic transcription at MIREX 2009. |
| doi_str_mv | 10.1109/TASL.2010.2093894 |
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This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset times, durations, and intensity of concurrent sounds in audio recordings, played by one or more instruments. Pitch estimation is carried out by means of a front-end that jointly uses a constant-Q and a bispectral analysis of the input audio signal; subsequently, the processed signal is correlated with a fixed 2-D harmonic pattern. Onsets and durations detection procedures are based on the combination of the constant-Q bispectral analysis with information from the signal spectrogram. The detection process is agnostic and it does not need to take into account musicological and instrumental models or other a priori knowledge. The system has been validated against the standard Real-World Computing (RWC)-Classical Audio Database. 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(IEEE) Aug 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-c177b65706ed8a98a8de93eb1b9f4d4b68d8067586b4a3556327a2b735f7630e3</citedby><cites>FETCH-LOGICAL-c421t-c177b65706ed8a98a8de93eb1b9f4d4b68d8067586b4a3556327a2b735f7630e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5640655$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54737</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5640655$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24413092$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Argenti, F</creatorcontrib><creatorcontrib>Nesi, P</creatorcontrib><creatorcontrib>Pantaleo, G</creatorcontrib><title>Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis</title><title>IEEE transactions on audio, speech, and language processing</title><addtitle>TASL</addtitle><description>In the area of music information retrieval (MIR), automatic music transcription is considered one of the most challenging tasks, for which many different techniques have been proposed. This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset times, durations, and intensity of concurrent sounds in audio recordings, played by one or more instruments. Pitch estimation is carried out by means of a front-end that jointly uses a constant-Q and a bispectral analysis of the input audio signal; subsequently, the processed signal is correlated with a fixed 2-D harmonic pattern. Onsets and durations detection procedures are based on the combination of the constant-Q bispectral analysis with information from the signal spectrogram. The detection process is agnostic and it does not need to take into account musicological and instrumental models or other a priori knowledge. The system has been validated against the standard Real-World Computing (RWC)-Classical Audio Database. The proposed method has demonstrated good performances in the multiple F 0 tracking task, especially for piano-only automatic transcription at MIREX 2009.</description><subject>Applied sciences</subject><subject>Audio signals</subject><subject>Audio signals processing</subject><subject>Bispectral analysis</subject><subject>bispectrum</subject><subject>constant-Q analysis</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Estimating</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Filter bank</subject><subject>Fourier transforms</subject><subject>Harmonic analysis</subject><subject>Hidden Markov models</subject><subject>higher order spectra</subject><subject>Information retrieval</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Mathematical models</subject><subject>Miscellaneous</subject><subject>Music</subject><subject>music information retrieval (MIR)</subject><subject>polyphonic music transcription</subject><subject>Psychoacoustic models</subject><subject>Recording</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal representation. Spectral analysis</subject><subject>Signal, noise</subject><subject>Spectral analysis</subject><subject>Studies</subject><subject>Tasks</subject><subject>Telecommunications and information theory</subject><issn>1558-7916</issn><issn>2329-9290</issn><issn>1558-7924</issn><issn>2329-9304</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1rGzEQhpeSQpM0PyD0shQKuayj74-jbZK24NCWOuQotFotUVivthrtwf8-MjY-9KLR8D4zDE9V3WK0wBjp--3y72ZBUGkJ0lRp9qG6xJyrRmrCLs5_LD5VVwBvCDEqGL6sXpZzjjubg6u3yY7gUphyiGMd-_p3HPbTaxxL9jRDeVcWfFeXML_6eh1HyHbMzZ96FWDyLic71MvRDnsI8Ln62NsB_M2pXlfPjw_b9Y9m8-v7z_Vy0zhGcG4clrIVXCLhO2W1sqrzmvoWt7pnHWuF6hQSkivRMks5F5RIS1pJeS8FRZ5eV3fHvVOK_2YP2ewCOD8MdvRxBoOFxEQjhVRBv_6HvsU5lXvBaMyk0ELQAuEj5FIESL43Uwo7m_YGI3MwbQ6mzcG0OZkuM99Oiy04O_TFowtwHiSMYYo0KdyXIxe89-eYC4YE5_Qd2UOF3Q</recordid><startdate>20110801</startdate><enddate>20110801</enddate><creator>Argenti, F</creator><creator>Nesi, P</creator><creator>Pantaleo, G</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110801</creationdate><title>Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis</title><author>Argenti, F ; Nesi, P ; Pantaleo, G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-c177b65706ed8a98a8de93eb1b9f4d4b68d8067586b4a3556327a2b735f7630e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applied sciences</topic><topic>Audio signals</topic><topic>Audio signals processing</topic><topic>Bispectral analysis</topic><topic>bispectrum</topic><topic>constant-Q analysis</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Estimating</topic><topic>Estimation</topic><topic>Exact sciences and technology</topic><topic>Filter bank</topic><topic>Fourier transforms</topic><topic>Harmonic analysis</topic><topic>Hidden Markov models</topic><topic>higher order spectra</topic><topic>Information retrieval</topic><topic>Information theory</topic><topic>Information, signal and communications theory</topic><topic>Mathematical models</topic><topic>Miscellaneous</topic><topic>Music</topic><topic>music information retrieval (MIR)</topic><topic>polyphonic music transcription</topic><topic>Psychoacoustic models</topic><topic>Recording</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal representation. Spectral analysis</topic><topic>Signal, noise</topic><topic>Spectral analysis</topic><topic>Studies</topic><topic>Tasks</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Argenti, F</creatorcontrib><creatorcontrib>Nesi, P</creatorcontrib><creatorcontrib>Pantaleo, G</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on audio, speech, and language processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Argenti, F</au><au>Nesi, P</au><au>Pantaleo, G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis</atitle><jtitle>IEEE transactions on audio, speech, and language processing</jtitle><stitle>TASL</stitle><date>2011-08-01</date><risdate>2011</risdate><volume>19</volume><issue>6</issue><spage>1610</spage><epage>1630</epage><pages>1610-1630</pages><issn>1558-7916</issn><issn>2329-9290</issn><eissn>1558-7924</eissn><eissn>2329-9304</eissn><coden>ITASD8</coden><abstract>In the area of music information retrieval (MIR), automatic music transcription is considered one of the most challenging tasks, for which many different techniques have been proposed. This paper presents a new method for polyphonic music transcription: a system that aims at estimating pitch, onset times, durations, and intensity of concurrent sounds in audio recordings, played by one or more instruments. Pitch estimation is carried out by means of a front-end that jointly uses a constant-Q and a bispectral analysis of the input audio signal; subsequently, the processed signal is correlated with a fixed 2-D harmonic pattern. Onsets and durations detection procedures are based on the combination of the constant-Q bispectral analysis with information from the signal spectrogram. The detection process is agnostic and it does not need to take into account musicological and instrumental models or other a priori knowledge. The system has been validated against the standard Real-World Computing (RWC)-Classical Audio Database. The proposed method has demonstrated good performances in the multiple F 0 tracking task, especially for piano-only automatic transcription at MIREX 2009.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TASL.2010.2093894</doi><tpages>21</tpages></addata></record> |
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| subjects | Applied sciences Audio signals Audio signals processing Bispectral analysis bispectrum constant-Q analysis Detection, estimation, filtering, equalization, prediction Estimating Estimation Exact sciences and technology Filter bank Fourier transforms Harmonic analysis Hidden Markov models higher order spectra Information retrieval Information theory Information, signal and communications theory Mathematical models Miscellaneous Music music information retrieval (MIR) polyphonic music transcription Psychoacoustic models Recording Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Spectral analysis Studies Tasks Telecommunications and information theory |
| title | Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis |
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