Predominant Musical Instrument Classification based on Spectral Features
Audio and Speech Processing
2020-04-23 v2 Machine Learning
Sound
Machine Learning
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.
Keywords
Cite
@article{arxiv.1912.02606,
title = {Predominant Musical Instrument Classification based on Spectral Features},
author = {Karthikeya Racharla and Vineet Kumar and Chaudhari Bhushan Jayant and Ankit Khairkar and Paturu Harish},
journal= {arXiv preprint arXiv:1912.02606},
year = {2020}
}
Comments
Appeared in Proceedings of SPIN 2020