CINECA IRIS Institutional Research Information System
IRIS è l’archivio della produzione scientifica della Fondazione Bruno Kessler.
EnglishThis 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.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte di FBK.
| Titolo: | A probabilistic approach to simultaneous extraction of beats and downbeats |
| Autori: | |
| Data di pubblicazione: | 2012 |
| 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. |
| Handle: | http://hdl.handle.net/11582/270622 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11582/270622
Copyright © 2021