Predominant Musical Instrument Classification based on Spectral Features

Autor:	Racharla, Karthikeya, Kumar, Vineet, Jayant, Chaudhari Bhushan, Khairkar, Ankit, Harish, Paturu
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound Statistics - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/SPIN48934.2020.9071125
Popis:	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. Comment: Appeared in Proceedings of SPIN 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.02606 Zobrazit plný text záznamu View this record from Arxiv