Merged-Output Hidden Markov Model for Score Following of MIDI Performance with Ornaments, Desynchronized Voices, Repeats and Skips
نویسندگان
چکیده
A score-following algorithm for polyphonic MIDI performances is presented that can handle performance mistakes, ornaments, desynchronized voices, arbitrary repeats and skips. The algorithm is derived from a stochastic performance model based on hidden Markov model (HMM), and we review the recent development of model construction. In this paper, the model is further extended to capture the multi-voice structure, which is necessary to handle note reorderings by desynchronized voices and widely stretched ornaments in polyphony. For this, we propose mergedoutput HMM, which describes performed notes as merged outputs frommultiple HMMs, each corresponding to a voice part. It is confirmed that the model yields a score-following algorithm which is effective under frequent note reorderings across voices and complicated ornaments.
منابع مشابه
A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments
We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-performance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the ind...
متن کاملOuter-Product Hidden Markov Model and Polyphonic MIDI Score Following
We present a polyphonic MIDI score-following algorithm capable of following performances with arbitrary repeats and skips, based on a probabilistic model of musical performances. It is attractive in practical applications of score following to handle repeats and skips which may be made arbitrarily during performances, but the algorithms previously described in the literature cannot be applied t...
متن کاملAutoregressive Hidden Semi-Markov Model of Symbolic Music Performance for Score Following
A stochastic model of symbolic (MIDI) performance of polyphonic scores is presented and applied to score following. Stochastic modelling has been one of the most successful strategies in this field. We describe the performance as a hierarchical process of performer’s progression in the score and the production of performed notes, and represent the process as an extension of the hidden semi-Mark...
متن کاملRobust Polyphonic Midi Score Following with Hidden Markov Models
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 sto...
متن کاملPitch Spelling with Conditionally Independent Voices
We introduce a new approach for pitch spelling from MIDI data based on a probabilistic model. The model uses a hidden sequence of variables, one for each measure, describing the local key of the music. The spellings in the voices evolve as conditionally independent Markov chains, given the hidden keys. The model represents both vertical relations through the shared key and horizontal voice-lead...
متن کامل