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acoustic-features

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Journal Articles
Music Perception (2019) 36 (4): 335–352.
Published: 01 April 2019
... effects of distortion and harmonic structure on acoustic features and perceived pleasantness of electric guitar chords. Extracting psychoacoustic parameters from guitar tones with Music Information Retrieval technology revealed that the level of distortion and the complexity of interval relations affects...
Journal Articles
Music Perception (2016) 34 (1): 104–117.
Published: 01 September 2016
...: Roger.Dean@westernsydney.edu.au music perception valence modeling acoustic features agency MODELING PERCEPTIONS OF VALENCE IN DIVERSE MUSIC: ROLES OF ACOUSTIC FEATURES, AGENCY, AND INDIVIDUAL VARIATION ROGER T. DEAN MARCS Institute, Western Sydney University, Penrith, Australia FREYA BAILES...
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Factor loadings of the <b>acoustic</b> <b>features</b> extracted from the audio signal, s...
Published: 01 September 2019
FIGURE 2. Factor loadings of the acoustic features extracted from the audio signal, showing the formation of clusters between the following features: Cluster 1—1 (RMS), 4 (spectral roughness), and 12 (spectral flux); Cluster 2—8 (spectral skewness) and 9 (spectral kurtosis); Cluster 3—2 (ZCR), 3 More
Images
Line plots of the mean values for two <b>acoustic</b> <b>features</b> for all tracks and ...
Published: 01 April 2019
FIGURE 3. Line plots of the mean values for two acoustic features for all tracks and the segments “pre-break,” “breakdown,” “build-up,” and “drop.” Above: Mean values of the audio amplitude. Below: Mean values of the spectral flux. FIGURE 3. Line plots of the mean values for two acoustic More
Journal Articles
Music Perception (1999) 16 (3): 327–363.
Published: 01 April 1999
.... M., & Halle, M. (1951/1961/1963). Preliminaries to speech analysis: The distinctive features and their correlates. Cambridge, MA: MIT Press [re- print of Acoustics Laboratory, MIT, Technical report No. 13, with addenda et corrigenda, and supplement on "Tenseness and Laxness."] Kendall, R. A...
Journal Articles
Music Perception (2018) 36 (2): 217–242.
Published: 01 December 2018
..., we extracted 86 features ( acoustic features ) of the excerpts with the MIRtoolbox ( Lartillot & Toiviainen, 2007 ). First, we evaluated the perceptual and extracted acoustic features. Both perceptual and acoustic features posed statistical challenges (e.g., perceptual features were often...
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Spectrograms of each <b>acoustic</b> <b>feature</b> extracted from the audio signal separ...
Published: 01 September 2019
FIGURE 3. Spectrograms of each acoustic feature extracted from the audio signal separated in clusters (green dashed lines), showing the selected triggers (red lines) and the selected acoustic feature (in bold and underlined) for each cluster. FIGURE 3. Spectrograms of each acoustic feature More
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Vector navigation on the most discriminant axis of the <b>acoustic</b> <b>feature</b> spe...
Published: 01 September 2019
FIGURE 5. Vector navigation on the most discriminant axis of the acoustic feature spectral rolloff captured by LDA at the latency of 100 ms post stimulus: (Top) Reconstruction of the mean brain mapping when navigating along the most discriminant axis between the sample group of musicians (right More
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Vector navigation on the most discriminant axis of the <b>acoustic</b> <b>feature</b> roo...
Published: 01 September 2019
FIGURE 7. Vector navigation on the most discriminant axis of the acoustic feature root mean square (RMS) captured by LDA at the latency of 100 ms post stimulus: (Top) Reconstruction of the mean brain mapping when navigating along the most discriminant axis between the sample group of musicians More
Images
Vector navigation on the most discriminant axis of the <b>acoustic</b> <b>feature</b> spe...
Published: 01 September 2019
FIGURE 9. Vector navigation on the most discriminant axis of the acoustic feature spectral kurtosis captured by LDA at the latency of 100 ms post stimulus: (Top) Reconstruction of the mean brain mapping when navigating along the most discriminant axis between the sample group of musicians (right More
Journal Articles
Music Perception (2019) 37 (1): 42–56.
Published: 01 September 2019
...FIGURE 2. Factor loadings of the acoustic features extracted from the audio signal, showing the formation of clusters between the following features: Cluster 1—1 (RMS), 4 (spectral roughness), and 12 (spectral flux); Cluster 2—8 (spectral skewness) and 9 (spectral kurtosis); Cluster 3—2 (ZCR), 3...
Journal Articles
Music Perception (2006) 24 (2): 177–188.
Published: 01 December 2006
...Fabien Gouyon; Gerhard Widmer; Xavier Serra; Arthur Flexer This article brings forward the question of which acoustic features are the most adequate for identifying beats computationally in acoustic music pieces. We consider many different features computed on consecutive short portions of acoustic...
Journal Articles
Music Perception (2018) 35 (5): 540–560.
Published: 01 June 2018
... factors, passages were classified into nine previously defined expressive categories. Passages containing acoustic features associated with the “sad/relaxed” expressive category were twice as likely to employ solo texture. Moreover, a regression model incorporating all factors significantly predicted solo...
Journal Articles
Music Perception (2012) 30 (1): 49–70.
Published: 01 September 2012
...Tuomas Eerola; Rafael Ferrer; Vinoo Alluri considerable effort has been made towards understanding how acoustic and structural features contribute to emotional expression in music, but relatively little attention has been paid to the role of timbre in this process. Our aim was to investigate the...
Journal Articles
Music Perception (2015) 32 (4): 322–343.
Published: 01 April 2015
... identify the makam from purely acoustical features, and, when possible, to determine the relative importance of the various audible features that may be used to establish the makam. Two basic classes of features are investigated: perde (the set of pitches used in the performance) and seyir (which relates...
Journal Articles
Music Perception (2012) 29 (3): 297–310.
Published: 01 February 2012
... number of perceptual dimensions in the timbre space for music from one's own culture. Factor analyses of Indian and Western participants' ratings resulted in highly similar factor solutions. The acoustic features that predicted the perceptual dimensions were similar across the two participant groups...
Journal Articles
Music Perception (2010) 27 (3): 223–242.
Published: 01 February 2010
..., Experiment 1, to devise a framework of subjective rating scales for quantifying the perceptual qualities of polyphonic timbre and Experiment 2, to rate short excerpts of Indian popular music and correlate them with computationally extracted acoustic features. A factor analysis of the ratings suggested three...
Journal Articles
Music Perception (2019) 36 (4): 371–389.
Published: 01 April 2019
...FIGURE 3. Line plots of the mean values for two acoustic features for all tracks and the segments “pre-break,” “breakdown,” “build-up,” and “drop.” Above: Mean values of the audio amplitude. Below: Mean values of the spectral flux. FIGURE 3. Line plots of the mean values for two acoustic...
Journal Articles
Music Perception (2020) 37 (5): 373–391.
Published: 10 June 2020
... features, and that rondos would involve more acoustic cues for happiness (e.g., higher average pitch height and higher average attack rate). In a corpus analysis, we examined paired movement openings from 180 instrumental works, composed between 1770 and 1799. Rondos had significantly higher pitch height...
Journal Articles
Music Perception (2015) 32 (4): 394–412.
Published: 01 April 2015
... previously determined semantic labels auditory texture and luminance featured the highest associa- tions with perceptual dimensions for both languages. Auditory mass failed to show any strong correlations. Acoustic analysis identified energy distribution of har- monic partials, spectral detail...