基于AR预滤波器改进的l2范数正则化TV-CAR语音分析F0估计

2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI:10.1109/ISPACS51563.2021.9651030

K. Funaki

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引用次数: 0

摘要

我们已经提出了一种可以抑制时域和频域快速频谱变化的解析信号的l2范数正则化TV-CAR语音分析方法。我们已经用F0估计评估了加性高斯白噪声或粉红噪声的噪声损坏语音的性能。IRAPT算法对分析信号估计的复AR残差进行F0估计。我们发现骨传导(BC)预滤波器可以提高性能，因为BC滤波器可以抑制额外的噪声。BC特性是低通滤波器的特性;因此，一阶AR滤波器可以模拟BC滤波器。本文引入一阶和二阶AR滤波器作为预滤波器，以提高F0估计性能。

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On an improvement of F0 estimation based on ℓ2-norm regularized TV-CAR speech analysis using AR pre-filter

We have already proposed ℓ2-norm regularized TV-CAR speech analysis for an analytic signal that can suppress rapid spectral changes in time-domain and frequency-domain. We have already evaluated the performance using F0 estimation for noise corrupted speech with additive white Gauss noise or Pink noise. The IRAPT algorithm implemented the F0 estimation for the estimated complex AR residual from an analytic signal. We have found that a bone-conducted (BC) pre-filter makes it possible to improve the performance since the BC filter can suppress the additional noise. The BC characteristics are that of a low pass filter; as a result, a first-order AR filter can simulate the BC filter. This paper introduces first-order and second-order AR filters as the pre-filter to improve the F0 estimation performance.

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2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

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