A comparative study of multiple music signal noise reduction algorithms in music processing

Abstract

Electronic music is susceptible to noise in production, which needs to be processed. This paper analyzed several commonly used noise reduction algorithms, including wiener filtering, wavelet transform, spectral subtraction, and improved spectral subtraction, and then compared the noise reduction performance of several algorithms by producing noisy music datasets in the audio analysis tool librosa. It was found from the experimental results that the wavelet transform algorithm performed best when sym3 was used as the wavelet basis function, and the number of decomposition layers was 7. The comparison of different algorithms showed that the performance of the wiener filtering algorithm was poor in reducing noise, and the signal-to-noise ratio (SNR) and signal distortion ratio (SDR) was low; the performance of the improved spectral subtraction algorithm was the best, and the SNR and SDR were 20.36 dB and 17.94 dB, respectively, when the SNR was −8 dB, which were better than the other algorithms. The experimental results demonstrate the reliability of the improved spectral subtraction method in music signal noise reduction. The algorithm can be applied in practical music processing.