Time-Varying Complex AR Research Articles

Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO

Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO

Pitch estimation based on instantaneous robust algorithm for pitch tracking using weighted linear prediction-based complex speech analysis

Pitch estimation plays an important role on speech processing such as speech coding, synthesis, recognition, and so on. Although current pitch estimation method performs well under clean condition, the performance deteriorates significantly in noisy environment. For this reason, robust pitch estimation against additive noise is required. Modified auto-correlation method is commonly used as pitch estimation in which LP residual is used to compute the auto-correlation. We have previously proposed pitch estimation methods based on Time-Varying Complex AR (TV-CAR) analysis whose criterion is the weighted correlation of the complex residual obtained by the TV-CAR analysis, sum of the harmonics for the complex residual spectrum, or so on. On the other hand, Azarov et al. have proposed an improved method of RAPT (Robust Algorithm for Pitch Tracking) using an instantaneous harmonics that is called IRAPT (Instantaneous RAPT). The IRAPT can perform better estimation than RAPT. Since IRAPT uses band-limited analytic signal to obtain harmonic frequencies, the complex residual signal obtained by the TV-CAR analysis can also be applied to the IRAPT. In this paper, novel pitch estimation method using the instantaneous frequency based on the robust WLP (Weighted Linear Prediction) TV-CAR residual is proposed and evaluated.

Vandermonde transform for complex residual based on complex speech analysis

Vandermonde transform using the orthogonal basis is a time-frequency transform method that can describe a speech and audio signal to be uncorrelated and sparse, and can be considered to be also effective in speech processing. In particular, when applied to the residual considered to be effective in a pitch estimation of speech and speaker recognition. On the other hand, we have already proposed Time-Varying Complex AR (TV-CAR) speech analysis for an analytic signal. The TV-CAR analysis can improve spectral estimation accuracy in the low frequencies and it can estimate the complex AR coefficients for each sample. As a result, in the estimated complex residual signal, formant components are more subtracted from speech signal. It can perform better on pitch estimation, or so on. In this paper, we evaluated the time-frequency analysis using the Vandermonde transform based on an Orthogonal Matching Pursuit (OMP) method of the literature using the complex AR residual. The performance is evaluated for several orthogonal basis such as Fourier basis, Cos basis, K-L transform, and Gabor basis. Furthermore, the Ramanujan-sum is also introduced as the basis function to realize time-frequency transform.

F0 contour estimation based on time‐varying complex speech analysis

Robust F0 (Fundamental frequency) estimation plays an important role in speech processing such as speech coding and tonal speech recognition. We have already proposed robust F0 estimation algorithm based on time‐varying complex AR (TV‐CAR) speech analysis for analytic signal, in which the weighted autocorrelation function is calculated using the complex residual and then the F0 is searched as the peak sample for each frame [IEICE Trans. on Fundamentals, Vol.E91‐A.No.3] [IEICE Trans. on Fundamentals, Vol.E90‐A, No.8]. Although the algorithm can estimate more robust F0 estimation for IRS filtered speech corrupted by additive noise, the algorithm cannot perform better for non‐IRS filtered speech or slightly contaminated IRS‐filtered speech. In addition, the frame‐based F0 estimation cannot extract the F0 trajectories in the time‐domain. In order to cope with the drawbacks, this paper proposes quite simple F0 contour estimation algorithm based on the TV‐CAR speech analysis, in which the F0 contour is estimated by peak‐picking for the estimated time‐varying spectrum that is the same manner as formant frequency estimation. The experimental results demonstrate that the proposed method leads to more accurate continuous F0 estimation than the conventional one for high‐pitched speech due to the nature of analytic signal for non‐IRS filtered high‐pitched female speech.