Robust Polyphonic Midi Score Following with Hidden Markov Models

Diemo Schwarz (1) , Nicola Orio (1) , Norbert Schnell (1)

1 Equipe Interactions musicales temps-réel

Although modern audio score following systems work very well with low polyphony performances, they are still too imprecise with highly polyphonic instruments such as the piano, or the guitar. On the other hand, these instruments can easily output Midi information which shows that our work on robust Midi score following is still needed. We propose an adaptation to Midi input of our HMM-based stochastic audio score follower, focusing the attention on the piano as our test instrument. The acoustic salience of the Midi notes is modeled by an amplitude envelope, taking into account the sustain pedal, from which note match and attack probabilities are derived. Tests with a complex piano piece played with many errors showed a very high robustness.

Mots clés: score following; Hidden Markov Models; automatic accompaniment; probabilistic modeling; Midi

Mots clés: Informatique musicale

Domaines: Son [cs.SD]; Interface homme-machine [cs.HC]; Musique, musicologie et arts de la scène; Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]; Ingénierie assistée par ordinateur; Multimédia [cs.MM]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Autre [cs.OH]; Traitement du signal et de l'image [eess.SP]

Diemo Schwarz, Nicola Orio, Norbert Schnell. Robust Polyphonic Midi Score Following with Hidden Markov Models. International Computer Music Conference (ICMC), Nov 2004, Miami, United States. pp.1-1. ⟨hal-01161328⟩