A pitch detection method based on continuous wavelet transform for harmonic signal

Abstract

In order to track a rapid transient of pitch, a required frame length of some conventional pitch detection methods is too long. Although there are wavelet based pitch detection methods which require only a few periods of pitch for a frame, they are not robust enough against noise. This paper proposes a new pitch detection method which can work properly under noisy environments even if a frame duration is short. The proposed method consists of a power level detector, a signal analyzer, an autocorrelator, a voiced-unvoiced detector and a lag time interpolator. The signal analyzer is based on the continuous wavelet transform using a harmonic analyzing wavelet. Usage of the harmonic analyzing wavelet gives us more information about a pitch in a scalogram. Simulations of pitch detection for a harmonic chirp signal and speech signals are performed. Performances are compared with two conventional pitch detection methods, cepstrum and modified correlation methods. As a result, a performance of a pitch detection by the proposed method under a noisy environment is better than that of the other two conventional methods. In particular, the largest improvement of performance is obtained for male voices.

Pitch detection using real-time processing system based on the cluster system

“…According to the result, doubled pitch is estimated in some durations where a fundamental frequency is less than 90 Hz. The result has almost the same characteristics as that of simulation shown in the previous study [1]. Furthermore, result of pitch detection for speech is shown in Fig.…”

“…The previous study with respect to the pitch detection method, called the harmonic wavelet transform method [1] showed that it takes a longer processing time because the method uses continuous wavelet transform which requires heavy calculation cost due to many convolutions. The harmonic wavelet transform method consists of 5 blocks; a power level detector, a signal analyzer, an autocorrelator, a voiced-unvoiced detector and a lag time interpolator.…”

“…Following the final step is the same procedure as the previous study [1]. It cannot be parallelized due to the algorithm.…”

“…One of the algorithms is the pitch detection algorithm, called harmonic wavelet transform [1]. Performance of the harmonic wavelet transform method is better than those of conventional methods [2,3].…”

Speaker gender identification based on combining linear and nonlinear features

“…Therefore, a wide variety of algorithms for pitch detection have been proposed in the speech processing literature, such as the autocorrelation, cepstrum-based methods LPC method and Average magnitude difference function and so on [6]. Since the glottal closure is marked by a sharp discontinuity in the speech signal, it can in some sense be related to the edge detection problem in image processing.…”