Abstract
The advent of internet and the growing number of digital media has increased the necessity of Music Information Retrieval systems within which Music Classification is a prominent task. Here, we present methods to perform genre based classification over five different genre and mood based classification using a mood model that maps mood onto a two-dimensional space along axes of arousal and valence. Support vector machine and a feed-forward artificial neural network are applied to achieve an overall accuracy of 88% for genre based classification and 73% and 67% accuracy for the arousal and valence axes respectively in mood based classification.
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Shakya, A., Gurung, B., Thapa, M.S., Rai, M., Joshi, B. (2017). Music Classification Based on Genre and Mood. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_14
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DOI: https://doi.org/10.1007/978-981-10-6430-2_14
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