Multi-pitch estimation based on multi-scale product analysis, improved comb filter and dynamic programming

Abstract

There are many multi-pitch estimation methods, but most of them can't perform perfectly for intrusion pitch detection. For this reason, a new multi-pitch detection approach is proposed. This method consists on the autocorrelation function of the Multi-scale product calculation of the mixture signal, its filtered version by a rectangular improved comb filter and the dynamic programming of the residual signal spectral density. First, we analyze the composite speech. Then, we apply the autocorrelation on the multi-scale product (AMP). We find the first pitch which represents the dominant one. Then, we apply the rectangular comb filter which has adaptive amplitude to remove the resulting signal from the original one. We operate AMP on the residue to obtain a pitch estimation of the intrusion. To improve the residue pitch estimation, we apply the dynamic programming to the spectral density of the residual signal to get optimum pitches corresponding also to intrusion signal. After that, we compare the two resulting pitch residue series to choose the most appropriate. Finally, this method is evaluated using the Cooke database and is compared to other well-known techniques. Experimental results confirm the strength and the performance of the proposed approach.