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matching-methods · dannenberg2006-score-alignment-accompaniment

Revision 8ac675d7-7ece-4905-8a50-dadec98eca20 · 11/1/2026, 4:50:45 PM UTC
Claim ID
matching-methods
Assertion
Score matching can model the score as a graph of states and use the Viterbi algorithm (dynamic programming) to find the optimal alignment with noisy pitch estimates. Because multi-pitch estimation is unreliable, polyphonic alignment systems compare spectra predicted from the score with observed spectra, often reduced to 12-element chroma vectors. The earliest accompaniment systems matched note sequences; hidden Markov models later allowed training on rehearsals; another approach represents score position as a continuous probability density.
Quote
Readers are likely familiar with the Viterbi Algorithm, which uses dynamic programming to solve this problem without actually enumerating every path. [...] Unfortunately, multi-pitch estimation is unreliable and still an active research area, so today's score-alignment systems use representations based on the distributions of frequencies in the signal, also known as the spectrum. [...] A popular technique for comparing spectra from scores to spectra in music audio is to reduce the detailed spectrum to a 12-element chroma vector [...]. The earliest computer accompaniment systems matched note sequences rather than spectral data, reducing data rates and computation. [...] Hidden Markov models have been explored for score following, allowing systems to be trained using rehearsals to follow more accurately and to respond with less latency. [...] Another approach avoids the problem of discrete states by representing score position as a continuous probability density function.
Quote language
en
Locator
section: Offline Score Matching; Variations and Alternate Approaches
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