语音增强
计算机科学
语音识别
噪声测量
噪音(视频)
可理解性(哲学)
噪声功率
卡尔曼滤波器
数值噪声
残余物
梯度噪声
人工智能
模式识别(心理学)
降噪
算法
噪声地板
功率(物理)
物理
哲学
图像(数学)
认识论
量子力学
作者
Sujan Kumar Roy,Aaron Nicolson,Kuldip K. Paliwal
标识
DOI:10.21437/interspeech.2020-1551
摘要
The existing Kalman filter (KF) suffers from poor estimates of the noise variance and the linear prediction coefficients (LPCs) in real-world noise conditions.This results in a degraded speech enhancement performance.In this paper, a deep learning approach is used to more accurately estimate the noise variance and LPCs, enabling the KF to enhance speech in various noise conditions.Specifically, a deep learning approach to MMSEbased noise power spectral density (PSD) estimation, called DeepMMSE, is used.The estimated noise PSD is used to compute the noise variance.We also construct a whitening filter with its coefficients computed from the estimated noise PSD.It is then applied to the noisy speech, yielding pre-whitened speech for computing the LPCs.The improved noise variance and LPC estimates enable the KF to minimise the residual noise and distortion in the enhanced speech.Experimental results show that the proposed method exhibits higher quality and intelligibility in the enhanced speech than the benchmark methods in various noise conditions for a wide-range of SNR levels.
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