Affective Neural Responses Sonified through Labeled Correlation Alignment

Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue in...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 12; s. 5574
Hlavní autoři: Álvarez-Meza, Andrés Marino, Torres-Cardona, Héctor Fabio, Orozco-Alzate, Mauricio, Pérez-Nastar, Hernán Darío, Castellanos-Dominguez, German
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 14.06.2023
MDPI
Témata:
Acoustic Stimulation
Acoustics
Affect (Psychology)
Auditory Perception - physiology
Brain - physiology
Brain Mapping - methods
canonical correlation analysis
centered kernel alignment
Correlation analysis
Electroencephalography
Electroencephalography - methods
Emotions
Emotions - physiology
functional connectivity
Listening
Machine learning
Mediation
Music
Music - psychology
music-EEG creation
Neural networks
Neurophysiology
Physiology
Sound
functional connectivity
canonical correlation analysis
centered kernel alignment
music-EEG creation
ISSN:1424-8220, 1424-8220
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Shrnutí:Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23125574