A unified approach to real time audio-to-score and audio-to-audio alignment using sequential Montecarlo inference techniques

Montecchio, Nicola; Cont, Arshia

doi:10.1109/icassp.2011.5946373

Cited by 31 publications

(27 citation statements)

References 5 publications

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“…This iterative process allows many hypotheses to be followed in parallel. Montecchio and Cont (2011) investigate the ability of a particle filter to adapt to gradual and sudden tempo change. Duan and Pardo (2011) examine the use of particle filtering for score alignment using both pitch and chroma features.…”

Section: Score Following Systemsmentioning

confidence: 99%

Event-based Multitrack Alignment using a Probabilistic Framework

Robertson

Plumbley

2015

Journal of New Music Research

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This paper presents a Bayesian probabilistic framework for real-time alignment of a recording or score with a live performance using an event-based approach. Multitrack audio files are processed using existing onset detection and harmonic analysis algorithms to create a representation of a musical performance as a sequence of time-stamped events. We propose the use of distributions for the position and relative speed which are sequentially updated in real-time according to Bayes' theorem. We develop the methodology for this approach by describing its application in the case of matching a single MIDI track and then extend this to the case of multitrack recordings. An evaluation is presented that contrasts our multitrack alignment method with state-of-theart alignment techniques.

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Section: Score Following Systemsmentioning

confidence: 99%

Event-based Multitrack Alignment using a Probabilistic Framework

Robertson

Plumbley

2015

Journal of New Music Research

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“…Another closely related research area is real-time score following, which aims to synchronize positions between the audio and the digital score. Arzt and Widmer proposed to use online dynamic time warping with simple tempo models to follow the music [2], while Montecchio and Cont modeled the system as a state tracking problem with various probabilities carefully trained [9]. Distinguished from these works, MusicScore can not only track the live performance, but also identify wrongly played notes or tempo.…”

Section: Related Workmentioning

confidence: 99%

MusicScore

Liu

Feng

2012

Proceedings of the 20th ACM International Conference on Multimedia

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In this paper, we present our design of MusicScore, a professional grade application on the iOS platform for music composition and live performance tracking, used by composers and amateurs alike. As its foundation, we have designed and implemented a high-quality music engraver, capable of real-time interactive rendering on mobile devices, as well as an intuitive user interface based on multitouch, both built from scratch using Objective-C and Cocoa Touch. To make MusicScore appealing to the general population for their practice and fun, we have introduced a unique auditory capability to MusicScore, so that it can "listen" to and analyze live instrument performance in real time. In order to compensate for the imperfect audio sensing system on mobile devices, we have proposed a collaborative sensing solution to better capture music signals in real time. To maximize the accuracy of live progress tracking and performance evaluation using a mobile device, we have designed a collection of note detection and tempo-based note matching algorithms, using a combination of microphone and accelerometer sensors. Based on our real-world implementation of MusicScore, extensive evaluation results show that MusicScore can achieve acceptably low error ratios, even for music pieces performed by highly inexperienced players.

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“…However, a detailed discussion and a systematic evaluation of the effectiveness of the methods for audio score following have not been given in the literature. Score-following algorithms that can follow repeats/skips have been proposed in [5], [11], [15]. The targets of these algorithms are predetermined repeats/skips from and to specific score positions, and treatment of arbitrary repeats/skips is not discussed nor guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips

Nakamura

Sagayama

2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-This paper discusses real-time alignment of audio signals of music performance to the corresponding score (a.k.a. score following) which can handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips) in performances. This type of score following is particularly useful in automatic accompaniment for practices and rehearsals, where errors and repeats/skips are often made. Simple extensions of the algorithms previously proposed in the literature are not applicable in these situations for scores of practical length due to the problem of large computational complexity. To cope with this problem, we present two hidden Markov models of monophonic performance with errors and arbitrary repeats/skips, and derive efficient score-following algorithms with an assumption that the prior probability distributions of score positions before and after repeats/skips are independent from each other. We confirmed real-time operation of the algorithms with music scores of practical length (around 10000 notes) on a modern laptop and their tracking ability to the input performance within 0.7 s on average after repeats/skips in clarinet performance data. Further improvements and extension for polyphonic signals are also discussed.

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A unified approach to real time audio-to-score and audio-to-audio alignment using sequential Montecarlo inference techniques

Cited by 31 publications

References 5 publications

Event-based Multitrack Alignment using a Probabilistic Framework

Event-based Multitrack Alignment using a Probabilistic Framework

MusicScore

Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips

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