Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2062
Title: Musical note and instrument classification with likelihood-frequency-time analysis and support vector machines
Authors: Özbek, Mehmet Erdal
Delpha, Claude
Duhamel, Pierre
Keywords: Instruments
Automatic transcription
Classification performance
Correct classification ratios
Lift analysis
Signal processing
Issue Date: 2007
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Özbek, M. E., Delpha, C., and Duhamel, P. (2007). Musical note and instrument classification with likelihood-frequency-time analysis and support vector machines. Paper presented at the 15th European Signal Processing Conference, EUSIPCO 2007, Poznan, Poland, 3-7 September (pp.941-945). Piscataway, N.J.: IEEE
Abstract: In this paper, we analyze the classification performance of a likelihood-frequency-time (LiFT) analysis designed for partial tracking and automatic transcription of music using support vector machines. The LiFT analysis is based on constant-Q filtering of signals with a filter-bank designed to filter 24 quarter-tone frequencies of an octave. Using the LiFT information, features are extracted from the isolated note samples and classification of instruments and notes is performed with linear, polynomial and radial basis function kernels. Correct classification ratios are obtained for 19 instrument and 36 notes.
Description: 15th European Signal Processing Conference, EUSIPCO 2007; Poznan; Poland; 3 September 2007 through 7 September 2007
URI: http://hdl.handle.net/11147/2062
ISSN: 2219-5491
2219-5491
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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