Trend Analysis Based AMDF for Robust Pitch Detection of Speech Signals
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Abstract
In this paper, we focus on improving the AMDF pitch detection algorithm (PDA) rather than designing a complete pitch detection system including many complex modification stages. As a hot classical PDA, generating half or multiple pitch errors is a usual defect of AMDF, especially in noisy conditions. Based on a deep analysis of many existing improvements of AMDF, we summarize two modified frameworks and classify the most outstanding improvements into them. Then we propose a novel and simple modified framework for AMDF to conquer the defect of AMDF. For our framework, we also present
two kinds of falling trend extraction methods to obtain the proposed Trend Analysis based AMDF
(TAAMDF). Finally, Experiments on the Keele database are conducted to evaluate our framework.
Compared with some outstanding modified AMDFs and well-known ACF, modified AMDF based
on our framework shows the best performance especially its robustness to different noises.
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References
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