On the automatic identification of difficult examples for beat tracking: Towards building new evaluation datasets

Holzapfel, André; Davies, Matthew E. P.; Zapata, José R.; Oliveira, João Lobato; Gouyon, Fabien

doi:10.1109/icassp.2012.6287824

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The Mean Mutual Agreement (MMA) is computed by measuring the mean of all mutual agreements between all estimated beat tracker outputs . In [32] the properties of existing beat tracking evaluation measures [33] were reviewed for the purpose of measuring mutual agreement. Of these, the Information Gain approach [34] (InfGain) was shown to have a true zero value able to match low MMA (measured in bits) with unrelated beat sequences.…”

Section: Selection Methods and Measuring Mutual Agreementmentioning

confidence: 99%

Multi-Feature Beat Tracking

Zapata

Davies

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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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 of mutual 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.

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Section: Selection Methods and Measuring Mutual Agreementmentioning

confidence: 99%

Multi-Feature Beat Tracking

Zapata

Davies

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…We refer to Gouyon [84] for a review on rhythm description systems. Holzapfel et al [104] perform a comparative evaluation of beat tracking algorithms, finding that the main limitations of existing systems are to deal with non-percussive material (e.g., vocal music) with soft onsets, and to handle short-time deviations, varying tempo, and integrating knowledge on tempo perception (double or half errors) [171].…”

Section: Rhythmmentioning

confidence: 99%

Music Information Retrieval: Recent Developments and Applications

Schedl¹,

Urbano²

2014

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We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.

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“…When assuming a steady beat and a single global tempo, many automated methods yield accurate tempo estimates and beat tracking results [10,11]. However, the task becomes much more difficult when one deals with music with weak note onsets and local tempo changes [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, the task becomes much more difficult when one deals with music with weak note onsets and local tempo changes [6]. A discussion of difficult examples for beat tracking can also be found in [12,11]. Instead of extracting tempo and beat information explicitly, various spectrogram-like representations have been proposed for visualizing tempo-related information over time.…”

Section: Introductionmentioning

confidence: 99%

Novel audio features for capturing tempo salience in music recordings

Thoshkahna

Müller

Kulkarni

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In music compositions, certain parts may be played in an improvisational style with a rather vague notion of tempo, while other parts are characterized by having a clearly perceivable tempo. Based on this observation, we introduce in this paper some novel audio features for capturing tempo-related information. Rather than measuring the specific tempo of a local section of a given recording, our objective is to capture the existence or absence of a notion of tempo, a kind of tempo salience. By a quantitative analysis within an Indian music scenario, we demonstrate that our audio features capture the aspect of tempo salience well, while being independent of continuous fluctuations and local changes in tempo

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On the automatic identification of difficult examples for beat tracking: Towards building new evaluation datasets

Cited by 8 publications

References 8 publications

Multi-Feature Beat Tracking

Multi-Feature Beat Tracking

Music Information Retrieval: Recent Developments and Applications

Novel audio features for capturing tempo salience in music recordings

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