Article: A groundbreaking algorithm for deconstructing wind instrument rhythms via transformer architecture and DTW Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.79 pp.94 - 119 Abstract: Cross-era wind instrument performances are often accompanied by significant rhythmic distortions and the insertion of ornamental notes; traditional dynamic time warping struggles to handle such structural mismatches. This paper integrates transformers with weighted dynamic time warping: the transformer encoder captures long-range dependencies in the musical score to generate enhanced rhythmic features; weighted dynamic time warping dynamically adjusts the cost of local matching via a learnable weight matrix to achieve flexible alignment. on the musical instrument digital interface and audio edited for synchronous tracks and organisation dataset and a custom-built wind instrument subset, our method achieves a precision of 86.2% (a 7.7% improvement over dynamic time warping), a recall of 83.1% (a 6.2% improvement), and an normalised discounted cumulative gai @5 of 0.723 (a 12.8% improvement). The study demonstrates that the 'neural-symbolic' fusion strategy can effectively decompose cross-epochal rhythmic variations in wind music, providing new insights for music information retrieval. Inderscience Publishers - linking academia, business and industry through research

Title: A groundbreaking algorithm for deconstructing wind instrument rhythms via transformer architecture and DTW

Authors: Binbin Wang

Addresses: School of Music and Dance, Daqing Normal University, Daqing, 163000, China

Abstract: Cross-era wind instrument performances are often accompanied by significant rhythmic distortions and the insertion of ornamental notes; traditional dynamic time warping struggles to handle such structural mismatches. This paper integrates transformers with weighted dynamic time warping: the transformer encoder captures long-range dependencies in the musical score to generate enhanced rhythmic features; weighted dynamic time warping dynamically adjusts the cost of local matching via a learnable weight matrix to achieve flexible alignment. on the musical instrument digital interface and audio edited for synchronous tracks and organisation dataset and a custom-built wind instrument subset, our method achieves a precision of 86.2% (a 7.7% improvement over dynamic time warping), a recall of 83.1% (a 6.2% improvement), and an normalised discounted cumulative gai @5 of 0.723 (a 12.8% improvement). The study demonstrates that the 'neural-symbolic' fusion strategy can effectively decompose cross-epochal rhythmic variations in wind music, providing new insights for music information retrieval.

Keywords: transformer architecture; dynamic time warping; DTW; wind instrument rhythm decomposition; cross-generational performance.

DOI: 10.1504/IJICT.2026.155060

International Journal of Information and Communication Technology, 2026 Vol.27 No.79, pp.94 - 119

Received: 18 Mar 2026
Accepted: 14 May 2026
Published online: 24 Jul 2026 *

Title: A groundbreaking algorithm for deconstructing wind instrument rhythms via transformer architecture and DTW

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