Estimation of fundamental frequency of reverberant speech by utilizing complex cepstrum analysis

This paper reports comparative evaluations of twelve typical methods of estimating fundamental frequency (F_0) over huge speech-sound datasets in artificial reverberant environments. They involve several classic algorithms such as Cepstrum, AMDF, LPC, and modified autocorrelation algorithms. Other methods involve a few modern instantaneous amplitude- and/or frequency-based algorithms, such as STRAIGHT-TEMPO, IFHC, and PHIA. The comparative results revealed that the percentage of correct rates and SNRs of the estimated F_0s were reduced drastically as reverberation time increased. They also demonstrated that homomorphic (complex cepstrum) analysis and the concept of the source-filter model were relatively effective for estimating F_0 from reverberant speech. This paper thus proposes a new method of robustly and accurately estimating F_0s in reverberant environments, by utilizing the modulation transfer function (MTF) concept and the source-filter model in complex cepstrum analysis. The MTF concept is used in this method to eliminate dominant reverberant characteristics from observed reverberant speech. The source-filter model (liftering) is used to extract source information from the processed cepstrum. Finally, F_0s are estimated from them by using the comb-filtering method. Additive-comparative evaluation was carried out on the new approach with other typical methods. The results demonstrated that it was better than the previously reported techniques in terms of robustness and providing accurate F_0 estimates in reverberant environments