A Study on Feature Analysis for Musical Instrument Classification

Abstract

The Department of Information Science is one of seven departments that make up the School of Business at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported.The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. CopyrightCopyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. CorrespondenceThis paper represents work to date and may not necessarily form the basis for the authors' final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors would be pleased to receive correspondence in connection with any of the issues raised in this paper, or for subsequent publication details. Please write directly to the authors at the address provided below. (Details of final journal/conference publication venues for these papers are also provided on the Department's publications web pages: http://www.otago.ac.nz/informationscience/pubs/). Any other correspondence concerning the Series should be sent to the DPS Coordinator.

Unsupervised group feature selection for media classification

“…Few works exploit feature selection in the context of genre classifications [9,12,22,50,60], musical instrument classification [8,51], and emotion/mood classification [43,59], commonly using a single dataset only. Most approaches consider wrapper-based feature selection, i.e., the underlying feature selection process is supervised, maximizing the classification accuracy of a learning algorithm.…”

“…Existing filter-based feature selection approaches in the context of media classification employ primarily individual feature evaluation to assess the quality of each feature component. For example, Simmermacher et al [51] and Deng et al [8] exploit the performance of information gain, information gain ratio, and symmetrical uncertainty for musical instrument classification. The authors report that the information gain achieves comparable performance to the other two filter-based methods.…”

“…However, the authors employ only a small subset of the available data consisting of a well-defined selection of 6 instruments: piano, bassoon, cello, flute, sax, and violin. In contrast, the works by Deng et al [8] and Simmermacher et al [51] employ nearly the same dataset size and only omit two instruments: guitar and marimba. Still, the authors simplify the experi- mental settings and employ the same number of samples for the instruments within each category.…”

Human Perception and Recognition of Musical Instruments: A Review

“…Musical instrument Identification is edged on classification of single note (Monophonic), more than one instrument notes at a time (Polyphonic), distinction of instruments in continuous recording or Classification of family/genre. Musical instruments are classified into five families depending on the sound produced as percussion, brass, string, woodwind and keyboard [4], [7].…”

Wavelet ridges for musical instrument classification

“…The representative capability of our wavelet ridge based feature is evaluated in different musical instrument classification problems using support vector machine (SVM) classifiers (Cristianini and Shawe-Taylor 2000;Vapnik 1998) which have proven to demonstrate successful classification rates for audio and musical instrument classification (Deng et al 2008;Essid et al 2006;Lin et al 2005;Wieczorkowska and Kubera 2009). As the study is focused on wavelet representations, the importance is given to the comparison results with different wavelet mother functions.…”