Musical instrument recognition and classification using time encoded signal processing and fast artificial neural networks

Mazarakis, G; Tzevelekos, P; Kouroupetroglou, G

dc.contributor.author	Mazarakis, G	en
dc.contributor.author	Tzevelekos, P	en
dc.contributor.author	Kouroupetroglou, G	en
dc.date.accessioned	2014-03-01T02:44:07Z
dc.date.available	2014-03-01T02:44:07Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31685
dc.subject	Artificial Neural Network	en
dc.subject	Domain Analysis	en
dc.subject	Musical Instruments	en
dc.subject	Shape Analysis	en
dc.subject	Signal Processing	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Computational cost	en
dc.subject.other	Instrument recognition	en
dc.subject.other	Organ classification	en
dc.subject.other	Sound waveforms	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Computation theory	en
dc.subject.other	Frequency domain analysis	en
dc.subject.other	Musical instruments	en
dc.subject.other	Neural networks	en
dc.subject.other	Signal processing	en
dc.subject.other	Object recognition	en
dc.title	Musical instrument recognition and classification using time encoded signal processing and fast artificial neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11752912_26	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11752912_26	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. In this work, we describe a system for the recognition of musical instruments from isolated notes. We are introducing the use of a Time Encoded Signal Processing method to produce simple matrices from complex sound waveforms, for instrument note encoding and recognition. These matrices are presented to a Fast Artificial Neural Network (FANN) to perform instrument recognition with promising results in organ classification and reduced computational cost. The evaluation material consists of 470 tones from 19 musical instruments synthesized with 5 wide used synthesizers (Microsoft Synth, Creative SB Live! Synth, Yamaha VL-70m Tone Generator, Edirol Soft-Synth, Kontakt Player) and 84 isolated notes from 20 western orchestral instruments (Iowa University Database). © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11752912_26	en
dc.identifier.isi	ISI:000238053100024	en
dc.identifier.volume	3955 LNAI	en
dc.identifier.spage	246	en
dc.identifier.epage	255	en