Rhythm Quantization for Transcription

Cemgil, Ali Taylan; Kappen, Bert; Desain, Peter

doi:10.1162/014892600559218

Cited by 48 publications

(30 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, the same state transition model can be used for both audio and MIDI; only the measurement model needs to be elaborated. For MIDI data, the Tempogram can also be replaced by a rhythm quantizer (Cemgil et al, 2000). Another advantage is that, for a large class of related models efficient inference and learning algorithms are well understood (Ghahramani and Hinton, 1996).…”

Section: Discussion and Future Researchmentioning

confidence: 99%

On tempo tracking: Tempogram Representation and Kalman filtering

Cemgil

Kappen

Desain

et al. 2000

Journal of New Music Research

Self Cite

104

View full text Add to dashboard Cite

We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated from a MIDI performance by Kalman filtering and smoothing. We also introduce the Tempogram representation, a wavelet-like multiscale expansion of a real performance, on which the Kalman filter operates.

show abstract

Section: Discussion and Future Researchmentioning

confidence: 99%

On tempo tracking: Tempogram Representation and Kalman filtering

Cemgil

Kappen

Desain

et al. 2000

Journal of New Music Research

Self Cite

104

View full text Add to dashboard Cite

show abstract

“…Perception of complex rhythm must be based on simple rhythms in a principled way. Both Longuet-Higgins (1987) and Cemgil, Desain, and Kappen (2000) proposed this recursive metric subdivision; however, they assumed categories centered around mechanical timing. As we have shown here, even when one assumes mechanical performances, the perceptual categories may not align with them.…”

mentioning

confidence: 99%

The Bayesian Way to Relate Rhythm Perception and Production

Sadakata¹,

Desain²,

Honing³

2006

Music Perception

Self Cite

View full text Add to dashboard Cite

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. University of CaliforniaPress is collaborating with JSTOR to digitize, preserve and extend access to Music Perception: An Interdisciplinary Journal.

show abstract

“…One of these methods has been described by Cemgil et al (2000) and was extended later by (Cemgil et al 2001) in order to take care of dynamic changes of the tempo during time. Alternatively, Cemgil and Kappen (2003) propose some Monte Carlo methods for tempo tracking and Whiteley et al (2006) are using Bayesian models of temporal structures.…”

Section: Estimation Of Relative Length Of Notes and Metermentioning

confidence: 99%

Classification in music research

Weihs

Ligges

Mörchen

et al. 2007

ADAC

View full text Add to dashboard Cite

Since a few years, classification in music research is a very broad and quickly growing field. Most important for adequate classification is the knowledge of adequate observable or deduced features on the basis of which meaningful groups or classes can be distinguished. Unsupervised classification additionally needs an adequate similarity or distance measure grouping is to be based upon. Evaluation of supervised learning is typically based on the error rates of the classification rules. In this paper we first discuss typical problems and possible influential features derived from signal analysis, mental mechanisms or concepts, and compositional structure. Then, we present typical solutions of such tasks related to music research, namely for organization of music collections, transcription of music signals, cognitive psychology of music, and compositional structure analysis.

show abstract

Rhythm Quantization for Transcription

Cited by 48 publications

References 2 publications

On tempo tracking: Tempogram Representation and Kalman filtering

On tempo tracking: Tempogram Representation and Kalman filtering

The Bayesian Way to Relate Rhythm Perception and Production

Classification in music research

Contact Info

Product

Resources

About