Predominant Musical Instrument Classification based on Spectral Features

TL;DR

This paper investigates musical instrument classification using spectral features on the IRMAS dataset, demonstrating that SVM classifiers achieve 79% accuracy and hierarchical clustering performs well among unsupervised methods.

Contribution

It compares supervised and unsupervised methods for instrument classification, highlighting the effectiveness of SVMs with spectral features.

Findings

SVM achieved 79% accuracy on IRMAS dataset

Hierarchical clustering performed well among unsupervised methods

Spectral features are effective for instrument classification

Abstract

This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for this purpose. The data includes musical clips recorded from various sources in the last century, thus having a wide variety of audio quality. We have presented a very concise summary of past work in this domain. Having implemented various supervised learning algorithms for this classification task, SVM classifier has outperformed the other state-of-the-art models with an accuracy of 79%. We also implemented Unsupervised techniques out of which Hierarchical Clustering has performed well.