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

<p>In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.</p>
</abstract>

In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Applied Mathematics and Nonlinear Sciences

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

In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features...

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 15. Apr. 2024

Akzeptiert: 28. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1017

Schlüsselwörter
RCNN, ReConv-EEG Net, Relief algorithm, Principal component analysis, Accompaniment perception

© 2024 Miaogu Liu et al., published by Sciendo

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

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 15. Apr. 2024

Akzeptiert: 28. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1017

SchlüsselwörterRCNN, ReConv-EEG Net, Relief algorithm, Principal component analysis, Accompaniment perception

© 2024 Miaogu Liu et al., published by Sciendo

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

Schlüsselwörter
RCNN, ReConv-EEG Net, Relief algorithm, Principal component analysis, Accompaniment perception