A Hierarchical Stratagem for Classification of String Instrument

Autor/inn/en	Ghosal, Arijit; Dutta, Suchibrota; Banerjee, Debanjan
Titel	A Hierarchical Stratagem for Classification of String Instrument
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 15 (2020) 1, S.1-23, Artikel 1 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
DOI	10.4018/IJWLTT.2020010101
Schlagwörter	Classification; Musical Instruments; Auditory Perception; Intonation; Acoustics; Music Activities + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Musikinstrument; Auditive Wahrnehmung; Akustische Wahrnehmung; Akustik; Musikalische Aktion
Abstract	Automatic recognition of instrument types from an audio signal is a challenging and a promising research topic. It is challenging as there has been work performed in this domain and because of its applications in the music industry. Different broad categories of instruments like strings, woodwinds, etc., have already been identified. Very few works have been done for the sub-categorization of different categories of instruments. Mel Frequency Cepstral Coefficients (MFCC) is a frequently used acoustic feature. In this work, a hierarchical scheme is proposed to classify string instruments without using MFCC-based features. Chroma reflects the strength of notes in a Western 12-note scale. Chroma-based features are able to differentiate from the different broad categories of string instruments in the first level. The identity of an instrument can be traced through the sound envelope produced by a note which bears a certain pitch. Pitch-based features have been considered to further sub-classify string instruments in the second level. To classify, a neural network, k-NN, Naïve Bayes' and Support Vector Machine have been used. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01