We propose a complex-domain pitch estimation algorithm for narrowband speech signals, which utilizes a complex spectrum containing both amplitude and phase spectrum information. Traditional frequency-domain pitch estimation algorithms assume that a speech signal has a harmonic structure; they estimate a pitch by calculating the distance between the adjacent peaks of the amplitude spectrum corresponding to harmonics. However, only a few peaks can be detected from narrowband speech signals because of their limited bandwidth, resulting in pitch estimation errors. In this article, phase differences between harmonics are utilized as an additional cue for pitch estimation. The phase difference between harmonics refers to the two-step phase difference between successive analysis frames and between the lowest-order harmonic and other harmonics, which is theoretically derived using the pitch. When the phase spectrum for the higher-order harmonic is shifted by the theoretical value, it agrees with the phase spectrum for the lowest-order harmonic. Therefore, for each pitch candidate, the proposed method calculates the shifted phase spectra and combines them with the amplitude spectrum to generate complex spectra. When a pitch candidate is correct, the cumulative sum of the complex spectra is added in the same direction, which emphasizes harmonics even for narrowband speech signals. Results of the objective evaluation show that the proposed method accurately estimates the pitch from narrowband speech signals.

This paper proposes a noise reduction method for reducing impact noise. The proposed method is based on a zero phase (ZP) signal which is defined as the IDFT of a spectral amplitude. The ZP signal of the impact noise has zero values except of around the origin, since its spectral amplitude is almost flat. When a speech signal has periodicity, its ZP signal has also periodicity. Hence, when the speech signal mixed with the impact noise, the noise can be reduced by replacing samples around the origin with samples around the second period of the ZP signal. A practical impact noise often has damped oscillation which is similar to a periodic signal. Under the assumption that the pitch of the damped oscillation is higher than one of speech, we detect and reduce the damped oscillation by introducing an additional pitch estimator into the ZP replacement method.

A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech degraded by additive background noise is proposed. Since a voiced speech can be represented as the stationary periodic signal over a short interval of time, most of voiced speech is predicted by the LPEF. On the other hand, when the input signal of the LPEF is a background noise, the prediction error signal becomes white. Assuming that the background noise is represented as generate by exciting a linear system with a white noise, then we can reconstruct the background noise from the prediction error signal by estimating the transfer function of noise generation system. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the noise which is reconstructed by the ADF from the speech degraded by additive background noise.