Article: A deep learning-based model for noise reduction and audio quality optimisation in music transmission signals Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.11 pp.29 - 37 Abstract: To address noise contamination, spectral compression, and reconstruction distortion encountered in wireless transmission, remote collaboration, and embedded audio devices, this paper proposes a deep learning-based framework for audio denoising and audio quality enhancement of music-oriented transmission signals. A dual-path convolutional network incorporating frequency-domain attention and perceptually guided composite loss is then designed to model long-term noise and transient musical details simultaneously. Distinct from speech-oriented models like DCCRN, SEGAN and U-Net, the proposed method fully leverages music-specific spectral dynamics and phase consistency for high-fidelity restoration under complex distortions. Evaluated using real and simulated noise scenarios with metrics including PESQ, STOI, SI-SNR and MOS-LQO, the model achieves a mean PESQ of 3.21 and an average SI-SNR improvement of 16.4 dB, outperforming baselines. Ablation and spectral visualisation validate the key modules. With strong adaptability, the framework is applicable to real-time remote music communication, collaborative systems and high-fidelity acoustic acquisition. Inderscience Publishers - linking academia, business and industry through research

Title: A deep learning-based model for noise reduction and audio quality optimisation in music transmission signals

Authors: Yang Song

Addresses: Department of Art and Physical Education, Huanghe University of Science and Technology, Zhengzhou, 450000, China

Abstract: To address noise contamination, spectral compression, and reconstruction distortion encountered in wireless transmission, remote collaboration, and embedded audio devices, this paper proposes a deep learning-based framework for audio denoising and audio quality enhancement of music-oriented transmission signals. A dual-path convolutional network incorporating frequency-domain attention and perceptually guided composite loss is then designed to model long-term noise and transient musical details simultaneously. Distinct from speech-oriented models like DCCRN, SEGAN and U-Net, the proposed method fully leverages music-specific spectral dynamics and phase consistency for high-fidelity restoration under complex distortions. Evaluated using real and simulated noise scenarios with metrics including PESQ, STOI, SI-SNR and MOS-LQO, the model achieves a mean PESQ of 3.21 and an average SI-SNR improvement of 16.4 dB, outperforming baselines. Ablation and spectral visualisation validate the key modules. With strong adaptability, the framework is applicable to real-time remote music communication, collaborative systems and high-fidelity acoustic acquisition.

Keywords: dual-path convolution; frequency-domain attention; perceptual loss optimisation; musical signal restoration; STFT-based feature modelling.

DOI: 10.1504/IJRIS.2026.152725

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.11, pp.29 - 37

Received: 30 Dec 2025
Accepted: 29 Jan 2026
Published online: 07 Apr 2026 *

Title: A deep learning-based model for noise reduction and audio quality optimisation in music transmission signals

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