Multi-pitch Detection From Ployphonic Music

With the development of computer networks and multimedia technology, multi-pitch detection from polyphonic music has become the key issue in the music signal processing.Firstly, this paper briefly introduces the fundamental concepts of multi-pitch detection, such as auditory system, auditory filters, wavelet transform, autocorrelation, STFT and MDCT. Secondly, it depicts the typical techniques of multi-pitch detection namely multi-pitch detection using auditory model, detection multi-pitch by Specmurt and detection multi-pitch by dyadic wavelet transform approximate coefficient multiply. Finally, typical techniques are improved and performance before and after modification is compared.Multi-pitch detection using auditory model is a traditional way. it utilizes auditory masking effects. It can be divided into three methods:multi-channel detection putting forward by Klapuri, high and low frequency multi-channel detection by M.Y.Wu, two-channel detection by Tolonen. Two-channel detection obtains better multi-pitch distribution than others. Multi-pitch detection using auditory model solves period distribution in three ways using normalized autocorrelation, autocorrelation by Fourier transform and enhanced autocorrelation which revises the traditional autocorrelation. Normalized autocorrelation reduces the influence of window function. Autocorrelation via Fourier transform enhances the saliency of peaks. While enhanced autocorrelation eliminates multiple periods.Detection multi-pitch by multiplication of approximate coefficients of dyadic wavelet transform enhances the saliency of peaks. Multi-pitch can be found more easily. In this paper, multiplications of different scale are compared by synthesized signal. The result confirms that approximate coefficients of the first three scales work best. Experimental results show that it improves the precision and recall.Compared with multi-pitch detection using auditory model, detection multi-pitch by Specmurt which uses music harmonic structure properly improves the resolution. Compared with wavelet approximate coefficient multiply, it has less interference frequency and is easier to eliminate outliers. Specmurt can be realized by MDCT, STFT, complex wavelet transform.This paper simulates three categories of multi-pitch detection discussed above and assesses their objective performance evaluation. The two objective evaluation indicators are precision and recall.