Noise Reduction Algorithm for Speech Enhancement

S. Pradeep Kumar, Anusha Daripelly, Sai Meghana Rampelli, Surya Kiran Reddy Nagireddy, Akhila Badishe, Amulya Attanthi
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引用次数: 1

Abstract

Speech communication involves transmitting information through speech between individuals or between individuals and machines in different areas such as speaker identification and automatic speech recognition. However, background noise can hinder effective communication by interfering with speech signals. Therefore, it is necessary to improve speech signals to minimize external disturbances. The process used to generate a more precise voice synthesis from an unclear audio source is called speech enhancement, which employs different algorithms to enhance speech quality. Wavelet transform is used to remove background noise from the messy audio while retaining essential speech information. To eliminate noise from the signal and achieve a clear signal, a semi-soft thresholding approach is employed, which removes chaotic coefficients from the wavelet. The primary objective of this paper is to use semi-soft thresholding to eliminate noise from signal and produce a clear signal. Noise reduction is a critical aspect of speech enhancement that has various applications, including speaker identification, prosthetic devices, VoIP, telepresence, and mobile devices.
语音增强的降噪算法
语音通信是指在说话人识别、语音自动识别等不同领域,通过语音在个体之间或个体与机器之间传递信息。然而,背景噪声会干扰语音信号,从而阻碍有效的通信。因此,有必要对语音信号进行改进,使外界干扰最小化。从不清晰的音频源生成更精确的语音合成的过程被称为语音增强,它采用不同的算法来提高语音质量。小波变换用于去除杂乱音频中的背景噪声,同时保留基本的语音信息。为了消除信号中的噪声,获得清晰的信号,采用半软阈值方法去除小波中的混沌系数。本文的主要目的是利用半软阈值去除信号中的噪声,产生清晰的信号。降噪是语音增强的一个关键方面,它有各种各样的应用,包括说话人识别、假肢设备、VoIP、远程呈现和移动设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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