Article: Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.5 pp.100 - 116 Abstract: The challenge of beat identification in piano teaching has progressively taken the stage as intelligent education technology develops quickly. Most of the conventional beat detection systems ignore the link between the video information and the player's motions by depending just on the analysis of auditory data. Based on the spatio-temporal two-branch attention mechanism, this work presents a piano beat detection model called TempoNet to increase the accuracy and robustness of beat identification. By means of the spatio-temporal dual-branching attention mechanism, the model efficiently captures the temporal features in the audio signal and the dynamic spatio-temporal features in the video signal by deep fusion of the two-modal information. Comparatively to conventional approaches, the suggested TempoNet model shows better beat identification accuracy and robustness according to experimental results on several test datasets. Inderscience Publishers - linking academia, business and industry through research

Title: Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention

Authors: Rigega Su

Addresses: Musicology and Musical Arts, National Academy of Music 'Pancho Vladigerov', 1142 Sofia, Bulgaria

Abstract: The challenge of beat identification in piano teaching has progressively taken the stage as intelligent education technology develops quickly. Most of the conventional beat detection systems ignore the link between the video information and the player's motions by depending just on the analysis of auditory data. Based on the spatio-temporal two-branch attention mechanism, this work presents a piano beat detection model called TempoNet to increase the accuracy and robustness of beat identification. By means of the spatio-temporal dual-branching attention mechanism, the model efficiently captures the temporal features in the audio signal and the dynamic spatio-temporal features in the video signal by deep fusion of the two-modal information. Comparatively to conventional approaches, the suggested TempoNet model shows better beat identification accuracy and robustness according to experimental results on several test datasets.

Keywords: piano teaching; beat recognition; spatio-temporal two-branch attentional mechanism; multimodal learning.

DOI: 10.1504/IJICT.2025.145149

International Journal of Information and Communication Technology, 2025 Vol.26 No.5, pp.100 - 116

Received: 31 Dec 2024
Accepted: 15 Jan 2025
Published online: 21 Mar 2025 *

Title: Piano teaching-assisted beat recognition based on spatio-temporal two-branch attention

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