Meter and Periodicity in Musical Performance

Sethares, William A.; Staley, Thomas W.

doi:10.1076/jnmr.30.2.149.7111

Cited by 27 publications

(16 citation statements)

References 30 publications

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“…The source code of Scheirer's system is publicly available. Sethares and Staley took a similar approach, but used a periodicity transform for periodicity analysis instead of a bank of comb filters [21]. Laroche proposed a noncausal algorithm where spectral change was measured as a function of time, the resulting signal was correlated with impulse trains of different periods, and dynamic programming was used to find a continuous time-varying tactus pulse [22].…”

Section: A Previous Workmentioning

confidence: 99%

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Analysis of the meter of acoustic musical signals

Klapuri

Eronen

Astola

2006

IEEE Trans. Audio Speech Lang. Process.

240

302

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Abstract-A method is decribed which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations.Index Terms-Acoustic signal analysis, music, musical meter analysis, music transcription.

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Section: A Previous Workmentioning

confidence: 99%

“…First, the degree of musical accentuation as a function of time has to be measured. Some systems do this in a continuous manner ( [20], [21]), whereas others extract discrete onsets from an audio signal ( [18], [24], [22]). Secondly, the periods and phases of the underlying metrical pulses have to be estimated.…”

Section: A Previous Workmentioning

confidence: 99%

Analysis of the meter of acoustic musical signals

Klapuri

Eronen

Astola

2006

IEEE Trans. Audio Speech Lang. Process.

240

302

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“…Scheirer [31] also used the idea of this subband-based beat tracking and applied a set of comb-filter resonators to the degrees of musical accentuation of six subbands to find the most resonant period. To locate periodicity in subband signals, Sethares and Staley [32] used a periodicity transform instead of using comb-filter resonators.…”

Section: ) Tracking Musical Beats (Quarter-note Level)mentioning

confidence: 99%

Content-Based Music Information Retrieval: Current Directions and Future Challenges

et al. 2008

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| The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web.Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content.Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.

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“…The source code of Scheirer's system is publicly available for testing. Sethares and Staley (2001) took a similar approach but used a periodicity transform for periodicity analysis instead of a bank of comb filters. Klapuri et al (to appear) proposed a system for estimating the meter of acoustic music signals at three time scales: the tactus, tatum, and musical measure pulse levels.…”

Section: More Recent Meter Estimation Systemsmentioning

confidence: 99%

Automatic Music Transcription as We Know it Today

Klapuri

2004

Journal of New Music Research

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The aim of this overview is to describe methods for the automatic transcription of Western polyphonic music. The transcription task is here understood as transforming an acoustic musical signal into a MIDI-like symbolic representation. Only pitched musical instruments are considered: recognizing the sounds of drum instruments is not discussed. The main emphasis is laid on estimating the multiple fundamental frequencies of several concurrent sounds. Various approaches to solve this problem are discussed, including methods that are based on modelling the human auditory periphery, methods that mimic the human auditory scene analysis function, signal model-based Bayesian inference methods, and data-adaptive methods. Another subproblem addressed is the rhythmic parsing of acoustic musical signals. From the transcription point of view, this amounts to the temporal segmentation of music signals at different time scales. The relationship between the two subproblems and the general structure of the transcription problem is discussed.

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Meter and Periodicity in Musical Performance

Cited by 27 publications

References 30 publications

Analysis of the meter of acoustic musical signals

Analysis of the meter of acoustic musical signals

Content-Based Music Information Retrieval: Current Directions and Future Challenges

Automatic Music Transcription as We Know it Today

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