Streamlined Tempo Estimation Based on Autocorrelation and Cross-correlation With Pulses

Percival, Graham; Tzanetakis, George

doi:10.1109/taslp.2014.2348916

Cited by 20 publications

(20 citation statements)

References 27 publications

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“…Throughout this article we will illustrate some observations with tempo estimates produced by three systems: perc (Percival and Tzanetakis, 2014), böck (Böck et al, 2015), 1 and schr (Schreiber and Müller, 2018b). They were chosen for illustrative purposes, their conceptual differences, and availability, not because they necessarily represent the state of the art.…”

Section: Introductionmentioning

confidence: 94%

“…We would like to illustrate the issue with two presumed applications of tempo estimation: similarity and recommendation (Tzanetakis and Percival, 2013;Percival and Tzanetakis, 2014;Böck et al, 2015). By definition, two recordings with the same tempo are similar-at least in this respect.…”

Section: Presumed Applicationsmentioning

confidence: 99%

“…OE 1 distributions can serve as indicators for the overall performance of a global tempo estimation system including the capability to help distinguish Based on values measured for Ballroom using a median ICBI-derived ground truth created from beat annotations by Krebs et al (2013). Ordered by year of publication (Scheirer, 1998;Klapuri et al, 2006;Davies et al, 2009;Oliveira et al, 2010;Gkiokas et al, 2012;Percival and Tzanetakis, 2014;Schreiber and Müller, 2014;Böck et al, 2015;Müller, 2017, 2018b). Estimates for zplane and echonest stem from Percival and Tzanetakis (2014).…”

Section: Formal Octave Errormentioning

confidence: 99%

See 2 more Smart Citations

Music Tempo Estimation: Are We Done Yet?

Schreiber

Urbano

Müller

2020

Transactions of the International Society for Music Information Retrieval

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With the advent of deep learning, global tempo estimation accuracy has reached a new peak, which presents a great opportunity to evaluate our evaluation practices. In this article, we discuss presumed and actual applications, the pros and cons of commonly used metrics, and the suitability of popular datasets. To guide future research, we present results of a survey among domain experts that investigates today's applications, their requirements, and the usefulness of currently employed metrics. To aid future evaluations, we present a public repository containing evaluation code as well as estimates by many different systems and different ground truths for popular datasets.

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Section: Introductionmentioning

confidence: 94%

Section: Presumed Applicationsmentioning

confidence: 99%

Section: Formal Octave Errormentioning

confidence: 99%

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Music Tempo Estimation: Are We Done Yet?

Schreiber

Urbano

Müller

2020

Transactions of the International Society for Music Information Retrieval

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“…In the next step, we accumulate all instance tempos evaluated from frames of the OSS. More details on this method are available in [11]. Since our algorithm is causal, we only use instance tempo produced by frames of the OSS which have appeared until now, in contrary to the method proposed in [11] which uses the entire frames.…”

Section: B Tempo Estimationmentioning

confidence: 99%

OBTAIN: Real-Time Beat Tracking in Audio Signals

Mottaghi¹,

Behdin²,

Esmaeili³

et al. 2017

IJSPS

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In this paper, we design a system in order to perform the real-time beat tracking for an audio signal. We use Onset Strength Signal (OSS) to detect the onsets and estimate the tempos. Then, we form Cumulative Beat Strength Signal (CBSS) by taking advantage of OSS and estimated tempos. Next, we perform peak detection by extracting the periodic sequence of beats among all CBSS peaks. In simulations, we can see that our proposed algorithm, Online Beat TrAckINg (OBTAIN), outperforms state-of-art results in terms of prediction accuracy while maintaining comparable and practical computational complexity. The real-time performance is tractable visually as illustrated in the simulations. 1

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“…Recent methods have focused on using neural networks to determine the beat onset curve and comb filtering to capture the periodicity [24]. Periodicity capture has also been done using autocorrelation methods [25].…”

Section: Introductionmentioning

confidence: 99%

Music Tempo Estimation Using Sub-Band Synchrony

2017

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Tempo estimation aims at estimating the pace of a musical piece measured in beats per minute. This paper presents a new tempo estimation method that utilizes coherent energy changes across multiple frequency sub-bands to identify the onsets. A new measure, called the sub-band synchrony, is proposed to detect and quantify the coherent amplitude changes across multiple sub-bands. Given a musical piece, our method first detects the onsets using the sub-band synchrony measure. The periodicity of the resulting onset curve, measured using the autocorrelation function, is used to estimate the tempo value. The performance of the sub-band synchrony based tempo estimation method is evaluated on two music databases. Experimental results indicate a reasonable improvement in performance when compared to conventional methods of tempo estimation.

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Streamlined Tempo Estimation Based on Autocorrelation and Cross-correlation With Pulses

Cited by 20 publications

References 27 publications

Music Tempo Estimation: Are We Done Yet?

Music Tempo Estimation: Are We Done Yet?

OBTAIN: Real-Time Beat Tracking in Audio Signals

Music Tempo Estimation Using Sub-Band Synchrony

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