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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Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5574
Main Authors:	Álvarez-Meza, Andrés Marino, Torres-Cardona, Héctor Fabio, Orozco-Alzate, Mauricio, Pérez-Nastar, Hernán Darío, Castellanos-Dominguez, German
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 14.06.2023 MDPI
Subjects:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Torres-Cardona, Héctor Fabio Pérez-Nastar, Hernán Darío Álvarez-Meza, Andrés Marino Orozco-Alzate, Mauricio Castellanos-Dominguez, German
AuthorAffiliation	2 Transmedia Research Center, Universidad de Caldas, Manizales 170003, Colombia; hector.torres_c@ucaldas.edu.co 1 Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia; morozcoa@unal.edu.co (M.O.-A.); hdperezn@unal.edu.co (H.D.P.-N.); cgcastellanosd@unal.edu.co (G.C.-D.)
AuthorAffiliation_xml	– name: 2 Transmedia Research Center, Universidad de Caldas, Manizales 170003, Colombia; hector.torres_c@ucaldas.edu.co – name: 1 Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia; morozcoa@unal.edu.co (M.O.-A.); hdperezn@unal.edu.co (H.D.P.-N.); cgcastellanosd@unal.edu.co (G.C.-D.)
Author_xml	– sequence: 1 givenname: Andrés Marino orcidid: 0000-0003-0308-9576 surname: Álvarez-Meza fullname: Álvarez-Meza, Andrés Marino – sequence: 2 givenname: Héctor Fabio orcidid: 0000-0001-9758-4038 surname: Torres-Cardona fullname: Torres-Cardona, Héctor Fabio – sequence: 3 givenname: Mauricio surname: Orozco-Alzate fullname: Orozco-Alzate, Mauricio – sequence: 4 givenname: Hernán Darío surname: Pérez-Nastar fullname: Pérez-Nastar, Hernán Darío – sequence: 5 givenname: German orcidid: 0000-0002-0138-5489 surname: Castellanos-Dominguez fullname: Castellanos-Dominguez, German
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SubjectTerms	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
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Title	Affective Neural Responses Sonified through Labeled Correlation Alignment
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