Automatic Bass Line Transcription from Streaming Polyphonic Audio

This paper proposes a method for the automatic transcription of the bass line in polyphonic music. The method uses a multiple-FO estimator as a front-end and this is followed by acoustic and musicological models. The acoustic modeling consists of separate models for bass notes and rests. The musicological model estimates the key and determines probabilities for the transitions between notes using a conventional bigram or a variable-order Markov model. The transcription is obtained with Viterbi decoding through the note and rest models. In addition, a causal algorithm is presented which allows transcription of streaming audio. The method was evaluated using 87 minutes of music from the RWC Popular Music Database. Recall and precision rates of 64% and 60%, respectively, were achieved for discrete note events.