A dataset and classification model for Malay, Hindi, Tamil and Chinese music

Autor:	Nahar, Fajilatun, Agres, Kat, BT, Balamurali, Herremans, Dorien
Rok vydání:	2020
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input. Both high level features, i.e., musically meaningful features, as well as low level features, i.e., spectrogram based features, were extracted from the audio files so as to optimize the performance of the different classification models. Comment: 4 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.04459 Zobrazit plný text záznamu View this record from Arxiv