Fundamental Frequency Estimation for Noisy Speech Using Entropy-Weighted Periodic and Harmonic Features

摘要

This paper proposes a robust method for estimating the fundamental frequency (F0) in real environments. It is assumed that the spectral structure of real environmental noise varies momentarily and its energy does not distribute evenly in the time-frequency domain. Therefore, segmenting a spectrogram of speech mixed with environmental noise into narrow time-frequency regions will produce low-noise regions in which the signal-to-noise ratio is high. The proposed method estimates F0 from the periodic and harmonic features that are clearly observed in the low-noise regions. It first uses two kinds of spectrogram, one with high frequency resolution and another with high temporal resolution, to represent the periodic and harmonic features corresponding to F0. Next, the method segments these two kinds of feature plane into narrow time-frequency regions, and calculates the probability function of F0 for each region. It then utilizes the entropy of the probability function as weight to emphasize the probability function in the low-noise region and to enhance noise robustness. Finally, the probability functions are grouped in each time, and F0 is obtained as the frequency with the highest probability of the function. The experimental results showed that, in comparison with other approaches such as the cepstrum method and the autocorrelation method, the developed method can more robustly estimate F0s from speech in the presence of band-limited noise and car noise.