eaR-package: Perception-based Music Analysis
In m-vidal/eaR: Perception-Based Auditory and Music Analysis
eaR-package R Documentation
Perception-based Music Analysis
Description
Analysis of auditory content and automatic extraction and description of musical features from auditory images, calculated from the acoustical signal.
Some functions were ported from earlier versions of IPEM Package in Matlab.
Details
Package: eaR
Type: Package
Version: 0.2.1
License: GPL-2
URL:
https://github.com/IPEM/eaR
Transformation of acoustic signals to auditory nerve images (ANI) can be realized with function CalcANI. Results of the model (a time/period image) can be accessed through plot function PlotImage. Some functions are intended for the creation of auditory stimuli for its subsequent transformation and analysis (AdaptLevel, CalcNoteFrequency, ShepardTone, ShepardToneComplex).
Further transformations from auditory nerve images can be taken using PeriodicityPitch and LeakyIntegration functions. Tonal inferences on periodicity pitch images can be performed with ContextualityIndex. This function calculates the goodness of fit of tone patterns (probe) for a given tonal context.
Additionally, RoughnessFFT analyses the roughness (or sensory dissonance) of a sound. Generally, plots of the analysis can be accessed on the generated object.
The rest of the functions have an accessory character, and can eventually be used in certain circumstances in combination to the main ones.
Author(s)
Marc Vidal, Marc Leman and Joren Six; credits are also due to Koen Tanghe, Micheline Lesaffre.
References
Leman, M. (2000). An auditory model of the role of short-term memory in probe-tone ratings. Music Perception, 17(4), pp. 481-509.
Leman, M. (2000). Visualization and calculation of the roughness of acoustical musical signals using the synchronization index model (SIM). Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-00), Verona, Italy.
Leman, M., Lesaffre, M., Tanghe, K. (2014).
Toolbox for perception-based music analysis. Matlab toolbox version 1.02 (beta). Manual, Ghent University.
Van Immerseel, L. Van and Martens, J. (1992). Pitch andvoiced/unvoiced determination with an auditory model. The Journal of the Acoustical Society of America, vol. 91, pp.3511-3526.
m-vidal/eaR documentation built on Nov. 18, 2022, 3:55 p.m.
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| eaR-package | R Documentation |
Perception-based Music Analysis
Description
Analysis of auditory content and automatic extraction and description of musical features from auditory images, calculated from the acoustical signal.
Some functions were ported from earlier versions of IPEM Package in Matlab.
Details
| Package: | eaR |
| Type: | Package |
| Version: | 0.2.1 |
| License: | GPL-2 |
| URL: | https://github.com/IPEM/eaR |
Transformation of acoustic signals to auditory nerve images (ANI) can be realized with function CalcANI. Results of the model (a time/period image) can be accessed through plot function PlotImage. Some functions are intended for the creation of auditory stimuli for its subsequent transformation and analysis (AdaptLevel, CalcNoteFrequency, ShepardTone, ShepardToneComplex).
Further transformations from auditory nerve images can be taken using PeriodicityPitch and LeakyIntegration functions. Tonal inferences on periodicity pitch images can be performed with ContextualityIndex. This function calculates the goodness of fit of tone patterns (probe) for a given tonal context.
Additionally, RoughnessFFT analyses the roughness (or sensory dissonance) of a sound. Generally, plots of the analysis can be accessed on the generated object.
The rest of the functions have an accessory character, and can eventually be used in certain circumstances in combination to the main ones.
Author(s)
Marc Vidal, Marc Leman and Joren Six; credits are also due to Koen Tanghe, Micheline Lesaffre.
References
Leman, M. (2000). An auditory model of the role of short-term memory in probe-tone ratings. Music Perception, 17(4), pp. 481-509.
Leman, M. (2000). Visualization and calculation of the roughness of acoustical musical signals using the synchronization index model (SIM). Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-00), Verona, Italy.
Leman, M., Lesaffre, M., Tanghe, K. (2014). Toolbox for perception-based music analysis. Matlab toolbox version 1.02 (beta). Manual, Ghent University.
Van Immerseel, L. Van and Martens, J. (1992). Pitch andvoiced/unvoiced determination with an auditory model. The Journal of the Acoustical Society of America, vol. 91, pp.3511-3526.
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