An HMM-based approach to humming transcription

Providing natural and efficient access to the fast growing multimedia information, accommodating a variety of user skills and preferences, is a critical aspect of content-based information mining. Query by humming provides a natural means for content-based retrieval from music databases. A statistical pattern recognition approach for recognizing hummed or sung melodies is reported in this paper. Being data-driven, the proposed system aims at providing a robust front-end especially for dealing with variability in user´s productions. The segment of a note in the humming waveform is modeled by a hidden Markov model (HMM) while data features such as pitch measures are modeled by Gaussian mixture models (GMM). Preliminary real-time recognition experiments are carried out based on humming data obtained from eight users and an overall correct recognition rate of around 80% is demonstrated.