Abstract
We often choose to listen to a song that suits our mood at that instant because an intimate relationship presents between music and human emotions. Thus, the automatic methods are needed to classify music by moods that have gained a lot of momentum in the recent years. It helps in creating library, searching music and other related application. Several studies on Music Information Retrieval (MIR) have also been carried out in recent decades. In the present task, we have built an unsupervised classifier for Hindi music mood classification using different audio related features like rhythm, timber and intensity. The dataset used in our experiment is manually prepared by five annotators and is composed of 250 Hindi music clips of 30 seconds that consist of five mood clusters. The accuracy achieved for music mood classification on the above data is 48%.
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Patra, B.G., Das, D., Bandyopadhyay, S. (2013). Unsupervised Approach to Hindi Music Mood Classification. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_7
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DOI: https://doi.org/10.1007/978-3-319-03844-5_7
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