Continuous Wavelet-like Transform Based Music Similarity Features for Intelligent Music Navigation

International audienceIntelligent music navigation is one of the important tasks in today's music applications. In this context we propose several high-level musical similarity features that can be used in automatic music navigation, classification and recommendation. The features we propose use Continuous Wavelet-like Transform as a basic time-frequency analysis of a musical signal due to its flexibility in time-frequency resolutions. A novel 2D beat histogram is presented in the paper as a rhythmic similarity feature which is free from dependency on recording condition and does not require sophisticated adaptive algorithms of threshold finding in beat detection. This paper also describes a CWT based algorithm of multiple F0 estimation (note detection) and corresponding melodic similarity features. Evaluation of the both similarity measures is done in automatic genre classification context and playlist composition