Pitch Analysis for Active Music Discovery

A significant proportion of commercial music is comprised of pitched content: a melody, a bass line, a famous guitar solo, etc. Consequently, algorithms that are capable of extracting and understanding this type of pitched content open up numerous opportunities for active music discovery, ranging from query-by-humming to musicalfeature-based exploration of Indian art music or recommendation based on singing style. In this talk I will describe some of my work on algorithms for pitch content analysis of music audio signals and their application to music discovery, the role of machine learning in these algorithms, and the challenge posed by the scarcity of labeled data and how we may address it.

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