Multidimensional humming transcription using a statistical approach for query by humming systems

A new statistical pattern recognition approach applied to human humming transcription is proposed in this research. A music note has two important attributes, i.e. pitch and duration. The proposed algorithm generates multidimensional humming transcriptions, which contain both pitch and duration information. Query by humming provides a natural means for content-based retrieval from music databases, and this research provides a robust front-end for such application. The segment of a note in the humming waveform is modeled by a hidden Markov model (HMM) while the pitch of the note is modeled by a pitch model using a Gaussian mixture model. Preliminary real-time recognition experiments are carried out with models trained by data obtained from eight human objects, and an overall correct recognition rate of around 80% is demonstrated.