Pitch estimation using harmonic product spectrum derived from DCT

Abstract

Estimation of pitch from a given segment of speech plays an eminent role in various speech processing applications, such as speech coding, speech recognition, speaker recognition tasks, speech synthesis, etc. Even though, there are several efficient algorithms, estimation of pitch frequency from speech signals that are severely degraded by noise is still a challenging task. In this paper, we propose a robust framework for pitch estimation using harmonic product spectrum (HPS) derived from discrete cosine transform (DCT) of the signal. This novel method exploits the better decorrelating nature of the DCT spectrum that enables the pitch harmonics to appear sharper in its spectrum. Potentially, this facilitates accurate pitch estimation at lower order of the harmonic product spectrum when compared with DFT-based HPS. Systematic evaluation is carried out to analyze the performance of the proposed method in comparison with some of the successful algorithms, like DFT-based HPS, SIFT, and Cepstrum-based technique. The results clearly show that the proposed algorithm outperforms the other algorithms for speech signals that are severely corrupted by noise (low SNR). The effectiveness of this method for different durations of analysis window, various orders of HPS, and the refinements are also discussed.

A Novel Approach for Improving Guitarists’ Performance Using Motion Capture and Note Frequency Recognition

“…The harmonic product spectrum was used, according to Equation (2) (HPS) [29], which is a refined version of DFT that takes a number of power spectra generated by a signal and multiplies them. The product disappears for non-pure signals, resulting in a much more accurate detection of guitar frequencies.…”

Note Frequency Recognition and Finger Motion Capture of a Guitarist: A Survey and Challenges Ahead

“…Most instrument tuning applications rely on discrete Fourier transformation (DFT), which generates a magnitude spectrum that describes the composition of frequencies that recognize the signal by the machine. We used a refined version called the harmonic product spectrum (HPS) [52], which takes several power spectrums generated by the signal and the product of the frequencies. Using HPS yielded better results as it reduced background noise and provided more accurate note labeling.…”

Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

“…The general expression governing the pitch estimation algorithm using the HPS derived from fast Fourier transform (FFT) is given in equation 11. 50 where, X is obtained by performing FFT of the signal, R is the number of harmonics considered. The frequency corresponding to the maximum value from the periodic correlation array Ytrue(ωtrue) is considered to be the pitch frequency.…”