This paper focuses on the automatic extraction of beat structure from a musical piece. A novel statistical approach to modeling beat sequences based on the application of Hidden Markov Models (HMM) is introduced. The resulting beat labels are obtained by running the Viterbi decoder and subsequent lattice rescoring. For the observation vectors we propose a new feature set that is based on the impulsive and harmonic components of the reassigned spectrogram. Different components of observation vectors have been investigated for their efficiency. The main advantage of the proposed approach is the absence of imposed deterministic rules. All the parameters are learned from the training data, and the experimental results show the efficiency of the proposed schema.
A probabilistic approach to simultaneous extraction of beats and downbeats
Khadkevich, Maksim;Omologo, Maurizio
2012-01-01
Abstract
This paper focuses on the automatic extraction of beat structure from a musical piece. A novel statistical approach to modeling beat sequences based on the application of Hidden Markov Models (HMM) is introduced. The resulting beat labels are obtained by running the Viterbi decoder and subsequent lattice rescoring. For the observation vectors we propose a new feature set that is based on the impulsive and harmonic components of the reassigned spectrogram. Different components of observation vectors have been investigated for their efficiency. The main advantage of the proposed approach is the absence of imposed deterministic rules. All the parameters are learned from the training data, and the experimental results show the efficiency of the proposed schema.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
