Title: Design of music signal enhancement system based on big data clustering technology

Authors: Taoru Kong; Yanli Shen

Addresses: School of Arts and Business, Xi'an Siyuan University, Xi'an, China ' Computer Engineering Technical College (Artificial Intelligence College), Guangdong Polytechnic of Science and Technology, Zhuhai, China

Abstract: Electronic music signals are prone to distortion due to noise interference. Therefore, an intelligent music signal enhancement system based on big data technology is designed. The system hardware consists of music signal acquisition module, audio processing module and audio codec module. It can extract the features of music interference signals based on big data clustering algorithm and use improved wavelet transform to denoise music signals. The music signal intensifier in the model adopts the autocorrelation filtering algorithm to filter the separated signals. This algorithm can separate the noiseless music signal from the noise signal. Finally, the test set music signals are input into the neural network. After the multi-dimensional feature vector of the signal passes through the hidden layer and the output layer, the class number of each music signal can be obtained and the automatic recognition of music signals can be realised. Experimental results show that the proposed method can reach 30.12 dB, the average gain coefficient is 0.987, and the bit error rate is 6.81%. The noise reduction performance and music signal enhancement effect are superior to the other two methods. Therefore, this method has certain practical value and application prospect.

Keywords: big data clustering; audio processing; improved wavelet transform; music signal enhancement.

DOI: 10.1504/IJCSYSE.2024.142767

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.182 - 191

Received: 07 Apr 2023
Accepted: 11 Jun 2023
Published online: 21 Nov 2024 *

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