Musical instruments signal analysis and recognition using fractal features

Zlatintsi, A; Maragos, P

dc.contributor.author	Zlatintsi, A	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:53:22Z
dc.date.available	2014-03-01T02:53:22Z
dc.date.issued	2011	en
dc.identifier.issn	22195491	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36269
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84863759052&partnerID=40&md5=72aef963d34eca4df16b01d9f1176dfa	en
dc.subject.other	Descriptors	en
dc.subject.other	Fractal feature	en
dc.subject.other	Gaussian mixture models	en
dc.subject.other	Hidden markov models (HMMs)	en
dc.subject.other	Mel-frequency cepstral coefficients	en
dc.subject.other	Multiple time scale	en
dc.subject.other	Multiscale complexity	en
dc.subject.other	Multiscale fractals	en
dc.subject.other	Music classification	en
dc.subject.other	Music signal analysis	en
dc.subject.other	Music signals	en
dc.subject.other	Static and dynamic	en
dc.subject.other	Wave forms	en
dc.subject.other	Fractal dimension	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Signal analysis	en
dc.subject.other	Computer music	en
dc.title	Musical instruments signal analysis and recognition using fractal features	en
heal.type	conferenceItem	en
heal.publicationDate	2011	en
heal.abstract	Analyzing the structure of music signals at multiple time scales is of importance both for modeling music signals and their automatic computer-based recognition. In this paper we propose the multiscale fractal dimension profile as a descriptor useful to quantify the multiscale complexity of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments. We compare the descriptiveness of our features against that of Mel frequency cepstral coefficients (MFCCs) using both static and dynamic classifiers, such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The methods and features proposed in this paper are promising for music signal analysis and of direct applicability in large-scale music classification tasks. © 2011 EURASIP.	en
heal.journalName	European Signal Processing Conference	en
dc.identifier.spage	684	en
dc.identifier.epage	688	en