@conference {9998,
	title = {A dataset and classification model for Malay, Hindi, Tamil and Chinese music},
	booktitle = {Workshop on Machine Learning and Music (MML 2020), at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) conference},
	year = {2020},
	abstract = {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.
},
	url = {https://arxiv.org/abs/2009.04459},
	author = {F Nahar and K Agres and B Balamurali and D Herremans}
}
@proceedings {2020,
	title = {Developing Music Technology for Emotion Regulation and Motor Rehabilitation},
	year = {2020},
	abstract = {Medical technology (MedTech) involving music is increasingly being developed for various clinical and non-clinical contexts to support health and well-being. This paper focuses on two recent systems that allow users to steer their own wellness and recovery: a BCI system for emotion self-regulation in listeners (Ehrlich, Agres, Guan, \& Cheng, 2019), and a motion-capture game for motor rehab (Agres \& Herremans, 2017).},
	keywords = {brain-computer interfaces, emotion regulation, Motor Rehabilitation, music \& medicine, Music MedTech, music technology, serious games},
	author = {Kat R. Agres}
}
