Musical Instrument Classification via Low-Dimensional Feature Vectors

TL;DR

This paper presents a white-box approach to classify musical instruments by analyzing timbre features using a combination of MFCC, LPC, and custom features, achieving improved accuracy over single-tool methods.

Contribution

It introduces a 32-dimensional feature vector and a naive all-pairs SVM for instrument classification, contrasting with neural network approaches.

Findings

Significant patterns distinguish different instrument timbres.

Custom features improve classification accuracy.

Performance varies across instruments, suggesting avenues for enhancement.

Abstract

Music is a mysterious language that conveys feeling and thoughts via different tones and timbre. For better understanding of timbre in music, we chose music data of 6 representative instruments, analysed their timbre features and classified them. Instead of the current trend of Neural Network for black-box classification, our project is based on a combination of MFCC and LPC, and augmented with a 6-dimensional feature vector designed by ourselves from observation and attempts. In our white-box model, we observed significant patterns of sound that distinguish different timbres, and discovered some connection between objective data and subjective senses. With a totally 32-dimensional feature vector and a naive all-pairs SVM, we achieved improved classification accuracy compared to a single tool. We also attempted to analyze music pieces downloaded from the Internet, found out different performance on different instruments, explored the reasons and suggested possible ways to improve the performance.