Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Abstract With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intellige...
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Autor*in:	Hajj, Nadine [verfasserIn] Filo, Maurice Awad, Mariette

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Composer recognition -grams Cortical algorithms

Anmerkung:	© The Author(s) 2017

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 4(2017), 1 vom: 08. Aug., Seite 55-65
Übergeordnetes Werk:	volume:4 ; year:2017 ; number:1 ; day:08 ; month:08 ; pages:55-65

Links:	Volltext

DOI / URN:	10.1007/s40747-017-0052-x

Katalog-ID:	SPR037218735

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spelling	10.1007/s40747-017-0052-x doi (DE-627)SPR037218735 (SPR)s40747-017-0052-x-e DE-627 ger DE-627 rakwb eng Hajj, Nadine verfasserin aut Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. Composer recognition (dpeaa)DE-He213 -grams (dpeaa)DE-He213 Cortical algorithms (dpeaa)DE-He213 Filo, Maurice aut Awad, Mariette (orcid)0000-0002-4815-6894 aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 4(2017), 1 vom: 08. Aug., Seite 55-65 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:4 year:2017 number:1 day:08 month:08 pages:55-65 https://dx.doi.org/10.1007/s40747-017-0052-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 08 08 55-65
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source	Enthalten in Complex & intelligent systems 4(2017), 1 vom: 08. Aug., Seite 55-65 volume:4 year:2017 number:1 day:08 month:08 pages:55-65
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author	Hajj, Nadine
spellingShingle	Hajj, Nadine misc Composer recognition misc -grams misc Cortical algorithms Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
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topic_title	Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms Composer recognition (dpeaa)DE-He213 -grams (dpeaa)DE-He213 Cortical algorithms (dpeaa)DE-He213
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title	Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
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title_full	Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
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title_sort	automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
title_auth	Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
abstract	Abstract With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. © The Author(s) 2017
abstractGer	Abstract With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. © The Author(s) 2017
abstract_unstemmed	Abstract With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. © The Author(s) 2017
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title_short	Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
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Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

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