Classifying musical instruments using speech signal processing methods

TLDR: Among the features used, after MFCC, ZCR proved to be the optimal feature for the classification of drum instrument, and the most significant feature for classifying Guitar, Violin and Drum is MFCC as it gives the better accurate results.

Abstract: Identification of musical instruments from the acoustic signal using speech signal processing methods is a challenging problem. Further, whether this identification can be carried out by a single musical note, like humans are able to do, is an interesting research issue that has several potential applications in the music industry. Attempts have been made earlier using the spectral and temporal features of the music acoustic signals. The process of identifying the musical instrument from monophonic audio recording basically involves three steps — pre-processing of music signal, extracting features from it and then classifying those. In this paper, we present an experiment-based comparative study of different features for classifying few musical instruments. The acoustic features, namely, the Mel-Frequency Cepstral Coefficients (MFCCs), Spectral Centroids (SC), Zero-Crossing Rate (ZCR) and signal energy are derived from the music acoustic signal using different speech signal processing methods. A Support Vector Machine (SVM) classifier is used with each feature for the relative comparisons. The classification results using different combinations of training by features from different music instrument and testing with another/same type of music instruments are compared. Our results indicate that the most significant feature for classifying Guitar, Violin and Drum is MFCC as it gives the better accurate results. Also, the feature which gives better accuracy results for the drum instrument is ZCR. Among the features used, after MFCC, ZCR proved to be the optimal feature for the classification of drum instrument.