<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this study, we enhanced the CaffeNet network for recognizing students’ facial expressions in a music classroom and extracted emotional features from their expressions. Additionally, students’ speech signals were processed through filters to identify emotional characteristics. Using the LRLR fusion strategy, these expression and speech-based emotional features were combined to derive multimodal fusion emotion results. Subsequently, a music teaching model incorporating this multimodal emotion recognition was developed. Our analysis indicates a mere 6.03% discrepancy between the model’s emotion recognition results and manual emotional assessments, underscoring its effectiveness. Implementation of this model in a music teaching context led to a noticeable increase in positive emotional responses—happy and surprised emotions peaked at 30.04% and 27.36%, respectively, during the fourth week. Furthermore, 70% of students displayed a positive learning status, demonstrating a significant boost in engagement and motivation for music learning. This approach markedly enhances student interest in learning and provides a solid basis for improving educational outcomes in music classes.</p>
</abstract>

In this study, we enhanced the CaffeNet network for recognizing students’ facial expressions in a music classroom and extracted emotional features from their expressions. Additionally, students’ speech signals were processed through filters to identify emotional characteristics. Using the LRLR fusion strategy, these expression and speech-based emotional features were combined to derive multimodal fusion emotion results. Subsequently, a music teaching model incorporating this multimodal emotion recognition was developed. Our analysis indicates a mere 6.03% discrepancy between the model’s emotion recognition results and manual emotional assessments, underscoring its effectiveness. Implementation of this model in a music teaching context led to a noticeable increase in positive emotional responses—happy and surprised emotions peaked at 30.04% and 27.36%, respectively, during the fourth week. Furthermore, 70% of students displayed a positive learning status, demonstrating a significant boost in engagement and motivation for music learning. This approach markedly enhances student interest in learning and provides a solid basis for improving educational outcomes in music classes.

Constructing a Multimodal Music Teaching Model in College by Integrating Emotions

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this study, we enhanced the CaffeNet network for recognizing students’ facial expressions in a music classroom and extracted emotional features from their...

Constructing a Multimodal Music Teaching Model in College by Integrating Emotions

Pubblicato online: 22 mag 2024

Pagine: -

Ricevuto: 25 gen 2024

Accettato: 03 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1202

Parole chiave
CaffeNet network, Speech signal, LRLR, Feature fusion, Music teaching

© 2024 Jia Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Constructing a Multimodal Music Teaching Model in College by Integrating Emotions

Pubblicato online: 22 mag 2024

Pagine: -

Ricevuto: 25 gen 2024

Accettato: 03 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1202

Parole chiaveCaffeNet network, Speech signal, LRLR, Feature fusion, Music teaching

© 2024 Jia Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
CaffeNet network, Speech signal, LRLR, Feature fusion, Music teaching