This article presents an offline method for aligning an audio signal to individual instrumental parts constituting a musical score. The proposed method is based on fitting multiple hidden semi-Markov models (HSMMs) to the observed audio signal. The emission probability of each state of the HSMM is described using latent harmonic allocation (LHA), a Bayesian model of a harmonic sound mixture. Each HSMM corresponds to one musical instrument’s part, and the state duration probability is conditioned on a linear dynamics system (LDS) tempo model. Variational Bayesian inference is used to jointly infer LHA, HSMM, and the LDS. We evaluate the capability of the method to align musical audio to its score, under reverberation, structural variations, and fluctuations in onset timing among different parts.

We present a method to recuperate fingerings for a given piece of violin music in order to recreate the timbre of a given audio recording of the piece. This is achieved by first analyzing an audio signal to determine the most likely sequence of two-dimensional fingerboard locations (string number and location along the string), which recovers elements of violin fingering relevant to timbre. This sequence is then used as a constraint for finding an ergonomic sequence of finger placements that satisfies both the sequence of notated pitch and the given fingerboard-location sequence. Fingerboard-location-sequence estimation is based on estimation of a hidden Markov model, each state of which represents a particular fingerboard location and emits a Gaussian mixture model of the relative strengths of harmonics. The relative strengths of harmonics are estimated from a polyphonic mixture using score-informed source segregation, and compensates for discrepancies between observed data and training data through mean normalization. Fingering estimation is based on the modeling of a cost function for a sequence of finger placements. We tailor our model to incorporate the playing practices of the violin. We evaluate the performance of the fingerboard-location estimator with a polyphonic mixture, and with recordings of a violin whose timbral characteristics differ significantly from that of the training data. We subjectively evaluate the fingering estimator and validate the effectiveness of tailoring the fingering model towards the violin.