Automatic segmentation of acoustic musical signals using hidden Markov models

Raphael, Christopher

doi:10.1109/34.761266

Cited by 137 publications

(80 citation statements)

References 6 publications

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“…Most previous studies rely on a prior training of this model, which can be instrumentspecific [10][11][12][13][14] or generic [16,17]. However, these methods requires relevant training data, which are not always available.…”

Section: Relation To Prior Workmentioning

confidence: 99%

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Off-line refinement of audio-to-score alignment by observation template adaptation

Joder

Schuller

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Audio-to-score alignment aims at matching a symbolic representation (the score) to a musical recording. A key problem in this application is the great variability of audio observations which can be explained by a single symbolic element. Whereas most previous works deal with this problem by training or heuristic design of a generic observation model, we propose the adaptation of this model to each musical piece. We exploit a template-based formulation of the observation model and we investigate two strategies for the adaptation of the templates using a Hidden Markov Model for the alignment.Experiments run on a large dataset of popular and classical piano music show that such an approach can lead to a significant improvement of the alignment accuracy compared to the use of a single generic model, even if the latter is trained on real data.

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Section: Relation To Prior Workmentioning

confidence: 99%

“…Some works use a prior learning of the observation model, with statistical [10][11][12] or template-based approaches [13,14]. In the latter approach, a template is built for each symbolic element, as the superposition of single-note templates.…”

Section: Introductionmentioning

confidence: 99%

Off-line refinement of audio-to-score alignment by observation template adaptation

Joder

Schuller

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Probabilistic methods were first attempted by Dannenberg (1997, 1998). This work paved the way for the use of Hidden Markov Models (HMM), a stochastic modelling technique, which has emerged as a promising way of implementing score following (Cano et al, 1999;Orio & Dechelle, 2001;Raphael, 1999Raphael, , 2001Orio et al, 2003;Schwarz et al, 2004;Pardo & Birmingham, 2005, Cont & Schwarz 2006b, Macrae & Dixon 2008. Other methods have also been attempted in recent years, such as using belief networks (Raphael, 2000) or graphical models (Raphael, 2004), but Hidden Markov Models are most widely adopted to date.…”

Section: Previous Score Following Researchmentioning

confidence: 99%

Investigating the Role of Score Following in Automatic Musical Accompaniment

Jordanous

Smaill

2009

Journal of New Music Research

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When suitable accompanists are not available to a soloist musician, an alternative possibility is to use computergenerated accompaniment. A computer accompanist should interact with the soloist and adapt to the soloist's playing as a human accompanist would, both reacting to expressive nuances of tempo and to unintentional errors such as wrong or mistimed notes. Over the past 25 years, accompaniment systems have been developed, all of which employ some form of score following: the process of following a musician's progress through the score of a piece during performance. This work considers the role of score following in automatic accompaniment. In this investigation we developed a computer accompanist that employs score following. Our computer musician uses Hidden Markov Models to model the score by metrical structure and to provide accompaniment to a soloist playing monophonic music in real time, as the soloist is playing. Working with MIDI input/output, it tracks tempo fluctuations, anticipates the soloist's next note and supports some amount of unintentional deviation from the score. Qualitative evaluation, by human testers, and quantitative evaluation, using measurable criteria taken from MIREX, reported that the system performs adequately. We then used interviews with eight human accompanists to consider how well a score following system models the accompaniment process. This evaluation raises questions about the musical interaction between soloist and accompanist that have received relatively little attention. The information we gathered from interviews suggests the importance of other aspects of accompaniment, such as the sharing of shape of the performance between musicians, rather than treating the accompanist as purely subservient. We discuss the implications of these issues for the design of automated accompanists.

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“…al. 1998), musical score following (Raphael 1999), and many others. In the HMM, recognition is often accomplished using dynamic programming (DP) to find the mostly likely sequence of hidden states given the observed data.…”

Section: Linear Dynamic Programming For Training In Sequence Estimationmentioning

confidence: 99%

Linear Dynamic Programming and the Training of Sequence Estimators

Raphael¹,

Nichols²

2009

Operations Research and Cyber-Infrastructure

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We consider the problem of finding an optimal path through a trellis graph when the arc costs are linear functions of an unknown parameter vector. In this context we develop an algorithm, Linear Dynamic Programming (LDP), that simultaneously computes the optimal path for all values of the parameter. We show how the LDP algorithm can be used for supervised learning of the arc costs for a dynamic-programming-based sequence estimator by minimizing empirical risk. We present an application to musical harmonic analysis in which we optimize the performance of our estimator by seeking the parameter value generating the sequence best agreeing with hand-labeled data.

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Automatic segmentation of acoustic musical signals using hidden Markov models

Cited by 137 publications

References 6 publications

Off-line refinement of audio-to-score alignment by observation template adaptation

Off-line refinement of audio-to-score alignment by observation template adaptation

Investigating the Role of Score Following in Automatic Musical Accompaniment

Linear Dynamic Programming and the Training of Sequence Estimators

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