  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.
