Fundamental Frequency Extraction of Noisy Speech Using Exponent Enhancement in Weighted Autocorrelation
Advances in intelligent systems and computing(2021)
摘要
This research proposes a powerful method for extracting fundamental frequencies from speech in noisy environments that is more successful for speech processing applications. This work discusses a noise-resistant method for fundamental frequency extraction based on an exponent augmentation in the weighted autocorrelation function. To demonstrate the greater accuracy for fundamental frequency extraction, we focus on the exponent of the magnitude difference function. According to experimental results, proposed approach’s presentation in noisy situations provides an unequaled presentation compared to the conventional technique when an appropriate exponent is used.
更多查看译文
关键词
Fundamental frequency,Weighted autocorrelation function,Autocorrelation,Magnitude difference function,Exponent
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要