A Review of Automatic Rhythm Description Systems

Gouyon, Fabien; Dixon, Simon

doi:10.1162/comj.2005.29.1.34

Cited by 93 publications

(70 citation statements)

References 53 publications

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“…Modelling the score by temporal units assisted us greatly with including beat tracking in the accompanist. Our implementation was simpler than much recent beat tracking work (Gouyon & Dixon, 2005) but was effective.…”

Section: Beat Tracking: For Monitoring the Soloist's Tempomentioning

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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Section: Beat Tracking: For Monitoring the Soloist's Tempomentioning

confidence: 99%

Investigating the Role of Score Following in Automatic Musical Accompaniment

Jordanous

Smaill

2009

Journal of New Music Research

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“…In order to interpret an audio recording in terms of such a structure (which is necessary in order to produce Western music notation), the first step is to determine the rate of the most salient pulse (or some measure of its central tendency), which is called the tempo. Algorithms used for tempo induction include autocorrelation, comb filterbanks, inter-onset interval histograms, Fourier transforms, and periodicity transform, which are applied to audio features such as an onset detection function [58]. The next step involves estimating the timing of the beats constituting the main pulse, a task known as beat tracking.…”

Section: Other Transcription Subtasksmentioning

confidence: 99%

“…Extensive research has been performed into beat tracking and rhythm parsing [58], but transcription rarely takes advantage of this knowledge. An exception is the transcription system in [70], which combines the processes of tempo and note onset estimation with that of note extraction, using a generative model for polyphonic spectrograms that simultaneously extracts the notes and the structure of a piece based on a 2-dimensional hierarchical tree-structured Bayesian model.…”

Section: Fusing Information Across the Aspects Of Musicmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

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“…In its most generic sense, rhythm refers to all of the temporal aspects of a musical work, whether represented in a score, measured from a performance, or existing only in the perception of the listener [16]. In the literature the concept of "automatic rhythm description" groups a number of applications as diverse as tempo induction, beat tracking, rhythm quantisation, meter induction and characterisation of timing deviations, to name but a few.…”

Section: Rhythmmentioning

confidence: 99%

“…Genre classification results a re greatly improved by using these high-level descriptors, showing the relevance of musically-meaningful representations for MIR tasks. For a more complete overview of the state of the art on rhythmic description and our own contributions towards a unified framework see [16].…”

Section: Rhythmmentioning

confidence: 99%

SIMAC: semantic interaction with music audio contents

Herrera

Bello

Widmer

et al. 2005

2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005)

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The SIMAC project addresses the study and development of innovative components for a music information retrieval system. The key feature is the usage and exploitation of semantic descriptors of musical content that are automatically extracted from music audio files. These descriptors are generated in two ways: as derivations and combinations of lower-level descriptors and as generalizations induced from manually annotated databases by the intensive application of machine learning. The project aims also towards the empowering (i.e. adding value, improving effectiveness) of music consumption behaviours, especially of those that are guided by the concept of similarity.

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A Review of Automatic Rhythm Description Systems

Cited by 93 publications

References 53 publications

Investigating the Role of Score Following in Automatic Musical Accompaniment

Investigating the Role of Score Following in Automatic Musical Accompaniment

Automatic music transcription: challenges and future directions

SIMAC: semantic interaction with music audio contents

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