Robust pitch detection of speech signals using steerable filters

Most of the well known and widely used pitch determination algorithms are frame-based. They only consider the speech local stationarity within the analysis frame. However, our novel pitch determination algorithms employ the steerable lters to obtain the direction of pitch change. Therefore, the proposed algorithms not only make full use of the information within an analysis frame, but also optimally utilize the information from neighbor frames by taking the advantage of the pitch direction. This allows us to use more than one frame to enhance pitch peaks for non-stationary, noisy speech signals. As a result, the proposed algorithms are superior to conventional methods in term of accuracy and reliability, and is robust to noise. Besides, the direction of pitch change can be estimated in di erent domains. Therefore, our algorithms can be applied in either time or frequency domain, or both of them.