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  1. Transactions on Audio, Speech and Language Processing (TASLP)
  2. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22
  3. Issue 4, April 2014
  4. Multi-feature beat tracking
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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22
Issue 12, December 2014
Issue 11, November 2014
Issue 10, October 2014
Issue 9, September 2014
Issue 8, August 2014
Issue 7, July 2014
Issue 6, June 2014
Issue 5, May 2014
Issue 4, April 2014
An overview of noise-robust automatic speech recognition
Application of Deep Belief Networks for natural language understanding
Low-rank approximation based multichannel wiener filter algorithms for noise reduction with application in cochlear implants
Design of superdirective planar arrays with sparse aperiodic layouts for processing broadband signals via 3-D beamforming
Multi-feature beat tracking
Investigation of speech separation as a front-end for noise robust speech recognition
Robust speaker identification in noisy and reverberant conditions
On the use of i-vector posterior distributions in probabilistic linear discriminant analysis
Chinese-English phone set construction for code-switching ASR using acoustic and DNN-extracted articulatory features
Issue 3, March 2014
Issue 2, February 2014
Issue 1, January 2014

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Multi-feature beat tracking

Content Provider ACM Digital Library
Author Gómez, Emilia Zapata, José R. Davies, Matthew E. P.
Copyright Year 2014
Abstract A recent trend in the field of beat tracking for musical audio signals has been to explore techniques for measuring the level of agreement and disagreement between a committee of beat tracking algorithms. By using beat tracking evaluation methods to compare all pairwise combinations of beat tracker outputs, it has been shown that selecting the beat tracker which most agrees with the remainder of the committee, on a song-by-song basis, leads to improved performance which surpasses the accuracy of any individual beat tracker used on its own. In this paper we extend this idea towards presenting a single, standalone beat tracking solution which can exploit the benefit ofmutual agreement without the need to run multiple separate beat tracking algorithms. In contrast to existing work, we re-cast the problem as one of selecting between the beat outputs resulting from a single beat tracking model with multiple, diverse input features. Through extended evaluation on a large annotated database, we show that our multi-feature beat tracker can outperform the state of the art, and thereby demonstrate that there is sufficient diversity in input features for beat tracking, without the need for multiple tracking models.
Starting Page 816
Ending Page 825
Page Count 10
File Format PDF
ISSN 23299290
e-ISSN 23299304
DOI 10.1109/TASLP.2014.2305252
Volume Number 22
Issue Number 4
Journal IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-04-01
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Beat tracking Evaluation Music information retrieval Music signal processing
Content Type Text
Resource Type Article
Subject Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology
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