Feature Analysis and Classification of Classical Musical Instruments: An Empirical Study

Abstract

is one of six 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. Copyright Copyright 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.

Chinese folk musical instruments recognition in polyphonic music

“…Christian et al [14] did empirical study on feature analysis for instrument recognition and found nineteen features selected from the Mel-frequency cepstral coefficients (MFCC) and the MPEG-7 audio descriptors achieved a recognition rate of around 94% by the best classifier assessed by cross validation. Liu [4] did a similar work finding the modified k-NN classifier compared to other ones such as GMMs using 19 features, in which 6 are temporal, 8 are spectral, and 5 are coefficients, achieves the highest accuracy of 93%.…”

Utilizing the Neuronal Behavior of Spiking Neurons to Recognize Music Signals Based on Time Coding Features

“…In the absence of an universally agreed set of music features on which to base classification, researchers have chosen their own feature sets, including those dealing with frequency, pitch, loudness and other aspects of spectral content. The same features are also typically used for polyphonic recognition [14]. Feature-based approaches using fuzzy clustering [15] and rule-based approaches [16] have also been tried.…”

Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments

“…The performance of the learning procedure is evaluated by classifying new sound samples (cross-validation). One of the most crucial aspects in the above procedure of instrument classification is to find the right features [2] and classifiers. Most of the research on audio signal processing has been focusing on speech recognition and speaker identification.…”