Abstract
In order to achieve an audio classification aimed to identify the composer, the use of adequate and relevant features is important to improve performance especially when the classification algorithm is based on support vector machines. As opposed to conventional approaches that often use timbral features based on a time-frequency representation of the musical signal using constant window, this paper deals with a new audio classification method which improves the features extraction according the Constant Q Transform (CQT) approach and includes original audio features related to the musical context in which the notes appear. The enhancement done by this work is also lay on the proposal of an optimal features selection procedure which combines filter and wrapper strategies. Experimental results show the accuracy and efficiency of the adopted approach in the binary classification as well as in the multi-class classification.
Highlights
Music Information Retrieval (MIR) is a growing field that benefits signal processing development and communication media tools
According to [14], the RFE-Support Vector Machines (SVM) algorithm can be decomposed into four steps: 1) Train an SVM on the training set; 2) Order features using the weights of the resulting classifier; 3) Eliminate features with the smallest weight; 4) Repeat the process with the training set restricted to the remaining features
3) Performance of features selection approachs: After the SVM optimization, we evaluate the efficiency of used features selection algorithms by comparing them according to the desired learning elements size
Summary
Music Information Retrieval (MIR) is a growing field that benefits signal processing development and communication media tools. It uses the pattern recognition techniques to solve problems of music digital transcription including classification process. This classification is aimed to identify the artist in a given musical track. In order to improve the performance, many researches are focused on the choice of an efficient classification algorithm, and on the use of relevant features which are able to answer questions in query. The rest of this paper is organized as follows: Section 2 reviews the previous works as state of the art on the automatic audio classification algorithms and the features selection.
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