Modeling the perception of tempo

Elowsson, Anders; Friberg, Anders

doi:10.1121/1.4919306

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…Source separation has previously been used as a pre-processing step for, e.g., the estimation of perceived speed and tempo in audio Elowsson and Friberg, 2015). An overview of the feature extraction is shown in Fig.…”

Section: Featuresmentioning

confidence: 99%

“…III C 9 is less susceptible to short, noisy peaks. This type of processing is common to get well-defined peaks in the SF-curve Elowsson and Friberg, 2015).…”

Section: Low Pass Filtering (Low Pass)mentioning

confidence: 99%

“…Some frequencies were not filtered with exactly the median value, but the order statistic instead varied with frequency. The exact values and details are specified by Elowsson and Friberg (2015), which give a more detailed overview of this source separation procedure.…”

Section: A Source Separationmentioning

confidence: 99%

“…With this transformation, the magnitude spectrum was converted to the decibel spectrum. The transformation is common (Elowsson and Friberg, 2015), as the decibel scale is more closely related to the perception of loudness than the raw magnitude values (Olson, 1972). If conversion to sound level is not applied, the SF will however be more sensitive to changes in magnitudes for the louder sounds, and this may be perceptually desirable to model performed dynamics.…”

Section: Computing the Log Of The Magnitude Spectrogram (Sl)mentioning

confidence: 99%

“…This operation is commonly applied to facilitate further processing on the one-dimensional vector (see, e.g., Elowsson and Friberg, 2015).…”

Section: Computing the Mean Across Frequency (Mean Freq)mentioning

confidence: 99%

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Predicting the perception of performed dynamics in music audio with ensemble learning

Elowsson

Friberg

2017

The Journal of the Acoustical Society of America

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By varying the dynamics in a musical performance, the musician can convey structure and different expressions. Spectral properties of most musical instruments change in a complex way with the performed dynamics, but dedicated audio features for modeling the parameter are lacking. In this study, feature extraction methods were developed to capture relevant attributes related to spectral characteristics and spectral fluctuations, the latter through a sectional spectral flux. Previously, ground truths ratings of performed dynamics had been collected by asking listeners to rate how soft/loud the musicians played in a set of audio files. The ratings, averaged over subjects, were used to train three different machine learning models, using the audio features developed for the study as input. The highest result was produced from an ensemble of multilayer perceptrons with an R 2 of 0.84. This result seems to be close to the upper bound, given the estimated uncertainty of the ground truth data. The result is well above that of individual human listeners of the previous listening experiment, and on par with the performance achieved from the average rating of six listeners. Features were analyzed with a factorial design, which highlighted the importance of source separation in the feature extraction.

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Section: Featuresmentioning

confidence: 99%

“…III C 9 is less susceptible to short, noisy peaks. This type of processing is common to get well-defined peaks in the SF-curve Elowsson and Friberg, 2015).…”

Section: Low Pass Filtering (Low Pass)mentioning

confidence: 99%

Section: A Source Separationmentioning

confidence: 99%

Section: Computing the Log Of The Magnitude Spectrogram (Sl)mentioning

confidence: 99%

“…This operation is commonly applied to facilitate further processing on the one-dimensional vector (see, e.g., Elowsson and Friberg, 2015).…”

Section: Computing the Mean Across Frequency (Mean Freq)mentioning

confidence: 99%

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Predicting the perception of performed dynamics in music audio with ensemble learning

Elowsson

Friberg

2017

The Journal of the Acoustical Society of America

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Perceptual Evaluation of Blended Sonification of Mechanical Robot Sounds Produced by Emotionally Expressive Gestures: Augmenting Consequential Sounds to Improve Non-verbal Robot Communication

Frid

Bresin

2021

Int J of Soc Robotics

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This paper presents two experiments focusing on perception of mechanical sounds produced by expressive robot movement and blended sonifications thereof. In the first experiment, 31 participants evaluated emotions conveyed by robot sounds through free-form text descriptions. The sounds were inherently produced by the movements of a NAO robot and were not specifically designed for communicative purposes. Results suggested no strong coupling between the emotional expression of gestures and how sounds inherent to these movements were perceived by listeners; joyful gestures did not necessarily result in joyful sounds. A word that reoccurred in text descriptions of all sounds, regardless of the nature of the expressive gesture, was “stress”. In the second experiment, blended sonification was used to enhance and further clarify the emotional expression of the robot sounds evaluated in the first experiment. Analysis of quantitative ratings of 30 participants revealed that the blended sonification successfully contributed to enhancement of the emotional message for sound models designed to convey frustration and joy. Our findings suggest that blended sonification guided by perceptual research on emotion in speech and music can successfully improve communication of emotions through robot sounds in auditory-only conditions.

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Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation

Park

Shin

Lee

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Modeling the perception of tempo

Cited by 8 publications

References 21 publications

Predicting the perception of performed dynamics in music audio with ensemble learning

Predicting the perception of performed dynamics in music audio with ensemble learning

Perceptual Evaluation of Blended Sonification of Mechanical Robot Sounds Produced by Emotionally Expressive Gestures: Augmenting Consequential Sounds to Improve Non-verbal Robot Communication

Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation

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