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

Intelligent 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.