Detecting Selected Instruments in the Sound Signal

Abstract

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors’ contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers.

Keywords

• convolutional neural network
• music information retrieval
• audio features
• sound analysis
• Mel-Frequency Cepstral Coefficients—<i>MFCC</i>
• recognizing musical instruments