Renato Panda

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We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and compare its performance to an approach using only standard audio (SA) features. We also analyse the fusion of both types of features. Employing only SA features, the best attained performance was 46.3%, while using only MA features the best outcome was 59.1%(More)
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We present a study on music emotion recognition from lyrics. We start from a dataset of 764 samples (audio+lyrics) and perform feature extraction using several natural language processing techniques. Our goal is to build classifiers for the different datasets, comparing different algorithms and using feature selection. The best results (44.2% F-measure)(More)
Large digital databases of Hindi music are available which creates an opportunity of filtering this data with multiple parameters. One of the most important parameter used by the listeners are their moods. This paper focuses on Automatic generation of mood based playlist for Hindi popular music with minimum user intervention. There are two major modules of(More)
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