A dataset and classification model for Malay, Hindi, Tamil and Chinese music
This work addresses music classification for specific ethnic groups in Singapore, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of classifying music by ethnic origin using a new dataset of Singaporean Chinese, Malay, Hindi, and Tamil music, achieving optimized performance by exploring high-level and low-level musical features.
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.