Noise Reduction Of Music Based On Robust Principal Component Analysis

With the development of science and technology and the rapid growth of the number of users in the recording device,the recording music in the life scene becomes more and more popular.So more and more companies offer music recognition and other services under the scene of life.However,in the scenes of life,the cheers,the voice,the sound of car horns and other noises often exist in the recording environment.That the retrieval of contaminated music has become an urgent problem to be solved.In order to meet the needs of the people to get more information from the music,the problem is needed to be solved rapidly.At present,the key problem of the above system is the denoising of noises in the life.In order to improve the quality of recording music in the life scene,the Low-Rank characteristic is applied to noise reduction.It presents a new method of reducing noise based on the Robust Principal Component Analysis(RPCA).This method provides a new idea for noise reduction in living sense.Firstly,based on the Low-Rank characteristic of solo music,the noise position detection of the music signal is preprocessed.The standard of noise position detection is the rank of music signal,when the rank of music signal is far greater than the rank of pure solo music,it is seen as music containing noise.Secondly,the Robust Principal Component Analysis is first applied to the field of music noise reduction.And the comparison with Wavelet de-noising and Independent Principal Component analysis is made.Wherein select the Augmented Lagrange Multipliters to solve the optimization problem of Robust Principal Component Analysis,introduce Perceptual Evaluation of Audio Quality and Signal-to-Noise Ratio as the evaluation criterion of comparison experiments.Experimental results show that the Signal-to-Noise Ratio of the music signal can be improved about 2~5dB.The Perceptual Evaluation of Audio Quality of the music is also improved.In summary,the noise reduction method based on Robust Principal Component Analysis has obvious advantages for the noise reduction of the living environment.