Pitch estimation of noisy speech using ensemble empirical mode decomposition and dominant harmonic modification

This paper presents an efficient pitch estimation algorithm (PEA) using dominant harmonic modification (DHM) and ensemble empirical mode decomposition (EEMD). The noisy speech is first low-pass filtered within the ranges of fundamental frequencies (50–500Hz) to obtain the pre-filtered signal (PFS). The pre-processed signal is then modified by enhancing its dominant harmonic and followed by the computation of the normalized autocorrelation function (NACF). Then, an EEMD based data adaptive time domain noise filtering is applied to the NACF. Finally, partial reconstruction is performed in the EEMD domain to determine the pitch period. Experimental evaluation of the proposed PEA shows that it outperforms some of the existing PEAs for a wide range of SNRs.