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

<p>In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.</p>
</abstract>

In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.

Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion

Applied Mathematics and Nonlinear Sciences

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

In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction...

Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 17. März 2024

Akzeptiert: 03. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1580

Schlüsselwörter
Loss function, Emotion feature vector, BiLSTM_BLS model, Piano playing

© 2024 Lingcao Wu., published by Sciendo

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

Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 17. März 2024

Akzeptiert: 03. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1580

SchlüsselwörterLoss function, Emotion feature vector, BiLSTM_BLS model, Piano playing

© 2024 Lingcao Wu., published by Sciendo

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

Schlüsselwörter
Loss function, Emotion feature vector, BiLSTM_BLS model, Piano playing