Abstract
In the absence of a music score, tempo can only be defined by its perception by users. Thus recent studies have focused on the estimation of perceptual tempo defined by listening experiments. So far, algorithms have only been proposed to estimate the tempo when people agree on it. In this paper, we study the case when people disagree on the perception of tempo and propose an algorithm to predict this disagreement. For this, we hypothesize that the perception of tempo is correlated to a set of variations of various viewpoints on the audio content: energy, harmony, spectral-balance variations and short-term-similarity-rate. We suppose that when those variations are coherent, a shared perception of tempo is favoured and when they are not, people may perceive different tempi.We then propose several statistical models to predict the agreement or disagreement in the perception of tempo from these audio features. Finally, we evaluate the models using a test-set resulting from the perceptual experiment performed at Last-FM in 2011.
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Notes
- 1.
Experiment 3 is performed on musical excerpts specifically chosen for their extremely slow or fast tempo and leads to a bi-modal distribution with peaks around 50 and 200Â bpm. Because of the specificities of these musical excerpts, we do not consider the results of it here.
- 2.
As explained in Sect. 2.1, we only keep the principal axes which explain more than 10 % of the overall variance. This leads to a final vector of 34-dimensions instead of 4*20 \(=\) 80 dimensions.
- 3.
It should be noted that for easiness of understanding we represent in Fig. 4 the features \(d_i(\lambda )\) while the \(\underline{C}\) is computed on \(d_i(b)\).
- 4.
The IQR is a measure of statistical dispersion, being equal to the difference between the upper and lower quartiles. It is considered more robust to the presence of outliers than the standard deviation.
- 5.
The log-scale is used to take into account the logarithmic character of tempo. In log-scale, the intervals [80–85] bpm and [160–170] bpm are equivalent.
- 6.
\(\text {Recall}=\frac{\text {True Positive}}{\text {True Positive + False Negative}}\).
- 7.
As opposed to Precision, the Recall is not sensitive on class distribution hence the mean-over-class-Recall is preferred over the F-Measure.
- 8.
It should be noted that we didn’t plot the relationship between \(T_{harmo}\) and the other estimated tempi because the effect we wanted to show was less clear. We will investigate why in the next paragraph.
- 9.
Firstly the test-set for our experiment and the one of [14] largely differ in their genre distribution. In [14], the tracks are equally distributed between classical, country, dance, hip-hop, jazz, latin, reggae, rock/pop and soul. In our test-set, most of the tracks are pop/rock tracks (50Â %), soul and country (about 10Â % each). The other genres represent less than 5Â % each. The experimental protocols also largely differ. Our test-set comes from a web experiment, done without any strict control on the users, whereas McKinney and Moelants had a rigorous protocol (lab experiment, chosen people). Users have then very different profiles. In McKinney and Moelants experiment, the 33 subjects had an average of 7Â years of musical education. In our case, we reckon that almost nobody had a musical training.
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Acknowledgments
This work was partly supported by the Quaero Program funded by Oseo French State agency for innovation and by the French government Programme Investissements d’Avenir (PIA) through the Bee Music Project.
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Peeters, G., Marchand, U. (2014). Predicting Agreement and Disagreement in the Perception of Tempo. In: Aramaki, M., Derrien, O., Kronland-Martinet, R., Ystad, S. (eds) Sound, Music, and Motion. CMMR 2013. Lecture Notes in Computer Science(), vol 8905. Springer, Cham. https://doi.org/10.1007/978-3-319-12976-1_20
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