Music type classification by spectral contrast feature

Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Dan-Ning Jiang, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao, Lian-Hong Cai
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 116 vol.1
container_issue
container_start_page 113
container_title
container_volume 1
creator Dan-Ning Jiang
Lie Lu
Hong-Jiang Zhang
Jian-Hua Tao
Lian-Hong Cai
description Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments show that the octave-based spectral contrast feature performs well in music type classification. Another comparison experiment demonstrates that the octave-based spectral contrast feature has a better discrimination among different music types than mel-frequency cepstral coefficients (MFCC), which is often used in previous music type classification systems.
doi_str_mv 10.1109/ICME.2002.1035731
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_1035731</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1035731</ieee_id><sourcerecordid>1035731</sourcerecordid><originalsourceid>FETCH-LOGICAL-c266t-7a11f234ed9957e5e9eedcfe2dff4d9f22333c0326dbc6026f88526e4f74703</originalsourceid><addsrcrecordid>eNotj8tqwzAQRQWlkJL6A0I3-gG7I40e1rKYtA0kZNHugyKPQMVNjKUs_Pc1NIcLZ3fhMLYR0AgB7nXXHbaNBJCNANQWxQOrnG1hGVoEpVasyvkHFtBp3cITM4dbToGXeSQeBp9ziin4kq4Xfp55HimUyQ88XC-Lc-GRfLlN9Mweox8yVXev2df79rv7rPfHj133tq-DNKbU1gsRJSrqndOWNDmiPkSSfYyqd1FKRAyA0vTnYECa2LZaGlLRKgu4Zi__r4mITuOUfv00n-5t-AcMwkSR</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Music type classification by spectral contrast feature</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Dan-Ning Jiang ; Lie Lu ; Hong-Jiang Zhang ; Jian-Hua Tao ; Lian-Hong Cai</creator><creatorcontrib>Dan-Ning Jiang ; Lie Lu ; Hong-Jiang Zhang ; Jian-Hua Tao ; Lian-Hong Cai</creatorcontrib><description>Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments show that the octave-based spectral contrast feature performs well in music type classification. Another comparison experiment demonstrates that the octave-based spectral contrast feature has a better discrimination among different music types than mel-frequency cepstral coefficients (MFCC), which is often used in previous music type classification systems.</description><identifier>ISBN: 9780780373044</identifier><identifier>ISBN: 0780373049</identifier><identifier>DOI: 10.1109/ICME.2002.1035731</identifier><language>eng</language><publisher>IEEE</publisher><subject>Asia ; Cepstral analysis ; Computer science ; Hidden Markov models ; History ; Mel frequency cepstral coefficient ; Modems ; Multiple signal classification ; Spatial databases ; Technology management</subject><ispartof>Proceedings. IEEE International Conference on Multimedia and Expo, 2002, Vol.1, p.113-116 vol.1</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c266t-7a11f234ed9957e5e9eedcfe2dff4d9f22333c0326dbc6026f88526e4f74703</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1035731$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,4048,4049,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1035731$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Dan-Ning Jiang</creatorcontrib><creatorcontrib>Lie Lu</creatorcontrib><creatorcontrib>Hong-Jiang Zhang</creatorcontrib><creatorcontrib>Jian-Hua Tao</creatorcontrib><creatorcontrib>Lian-Hong Cai</creatorcontrib><title>Music type classification by spectral contrast feature</title><title>Proceedings. IEEE International Conference on Multimedia and Expo</title><addtitle>ICME</addtitle><description>Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments show that the octave-based spectral contrast feature performs well in music type classification. Another comparison experiment demonstrates that the octave-based spectral contrast feature has a better discrimination among different music types than mel-frequency cepstral coefficients (MFCC), which is often used in previous music type classification systems.</description><subject>Asia</subject><subject>Cepstral analysis</subject><subject>Computer science</subject><subject>Hidden Markov models</subject><subject>History</subject><subject>Mel frequency cepstral coefficient</subject><subject>Modems</subject><subject>Multiple signal classification</subject><subject>Spatial databases</subject><subject>Technology management</subject><isbn>9780780373044</isbn><isbn>0780373049</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tqwzAQRQWlkJL6A0I3-gG7I40e1rKYtA0kZNHugyKPQMVNjKUs_Pc1NIcLZ3fhMLYR0AgB7nXXHbaNBJCNANQWxQOrnG1hGVoEpVasyvkHFtBp3cITM4dbToGXeSQeBp9ziin4kq4Xfp55HimUyQ88XC-Lc-GRfLlN9Mweox8yVXev2df79rv7rPfHj133tq-DNKbU1gsRJSrqndOWNDmiPkSSfYyqd1FKRAyA0vTnYECa2LZaGlLRKgu4Zi__r4mITuOUfv00n-5t-AcMwkSR</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Dan-Ning Jiang</creator><creator>Lie Lu</creator><creator>Hong-Jiang Zhang</creator><creator>Jian-Hua Tao</creator><creator>Lian-Hong Cai</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2002</creationdate><title>Music type classification by spectral contrast feature</title><author>Dan-Ning Jiang ; Lie Lu ; Hong-Jiang Zhang ; Jian-Hua Tao ; Lian-Hong Cai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c266t-7a11f234ed9957e5e9eedcfe2dff4d9f22333c0326dbc6026f88526e4f74703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Asia</topic><topic>Cepstral analysis</topic><topic>Computer science</topic><topic>Hidden Markov models</topic><topic>History</topic><topic>Mel frequency cepstral coefficient</topic><topic>Modems</topic><topic>Multiple signal classification</topic><topic>Spatial databases</topic><topic>Technology management</topic><toplevel>online_resources</toplevel><creatorcontrib>Dan-Ning Jiang</creatorcontrib><creatorcontrib>Lie Lu</creatorcontrib><creatorcontrib>Hong-Jiang Zhang</creatorcontrib><creatorcontrib>Jian-Hua Tao</creatorcontrib><creatorcontrib>Lian-Hong Cai</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dan-Ning Jiang</au><au>Lie Lu</au><au>Hong-Jiang Zhang</au><au>Jian-Hua Tao</au><au>Lian-Hong Cai</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Music type classification by spectral contrast feature</atitle><btitle>Proceedings. IEEE International Conference on Multimedia and Expo</btitle><stitle>ICME</stitle><date>2002</date><risdate>2002</risdate><volume>1</volume><spage>113</spage><epage>116 vol.1</epage><pages>113-116 vol.1</pages><isbn>9780780373044</isbn><isbn>0780373049</isbn><abstract>Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments show that the octave-based spectral contrast feature performs well in music type classification. Another comparison experiment demonstrates that the octave-based spectral contrast feature has a better discrimination among different music types than mel-frequency cepstral coefficients (MFCC), which is often used in previous music type classification systems.</abstract><pub>IEEE</pub><doi>10.1109/ICME.2002.1035731</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 9780780373044
ispartof Proceedings. IEEE International Conference on Multimedia and Expo, 2002, Vol.1, p.113-116 vol.1
issn
language eng
recordid cdi_ieee_primary_1035731
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Asia
Cepstral analysis
Computer science
Hidden Markov models
History
Mel frequency cepstral coefficient
Modems
Multiple signal classification
Spatial databases
Technology management
title Music type classification by spectral contrast feature
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T20%3A05%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Music%20type%20classification%20by%20spectral%20contrast%20feature&rft.btitle=Proceedings.%20IEEE%20International%20Conference%20on%20Multimedia%20and%20Expo&rft.au=Dan-Ning%20Jiang&rft.date=2002&rft.volume=1&rft.spage=113&rft.epage=116%20vol.1&rft.pages=113-116%20vol.1&rft.isbn=9780780373044&rft.isbn_list=0780373049&rft_id=info:doi/10.1109/ICME.2002.1035731&rft_dat=%3Cieee_6IE%3E1035731%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1035731&rfr_iscdi=true