Unsupervised Approach to Hindi Music Mood Classification

  • Braja Gopal Patra
  • Dipankar Das
  • Sivaji Bandyopadhyay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

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%.

Keywords

Hindi Music Music Mood Classification MIR Mood Taxonomy 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Braja Gopal Patra
    • 1
  • Dipankar Das
    • 2
  • Sivaji Bandyopadhyay
    • 1
  1. 1.Dept. of Computer Science & EngineeringJadavpur UniversityKolkataIndia
  2. 2.Dept. of Computer Science & EngineeringNIT MeghalayaIndia

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