联合去混响与残余回声抑制在会议系统中的应用

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"这篇论文是关于在嘈杂和混响环境中语音信号的联合去混响与残余回声抑制，出自IEEE Transactions on Audio, Speech, and Language Processing, 2008年11月第16卷第8期。作者包括Emanuël A.P. Habets, Sharon Gannot, Israel Cohen和Piet C.W. Sommen。文章指出，在免提设备中，由于环境噪声、房间混响和远端回声，接收的麦克风信号不仅包含近端语音，还包含多种干扰。为了解决这个问题，过去二十年里发展了后处理滤波器，与单麦克风声学回声消除器配合使用，以提升近端语音的清晰度和可理解性。尽管先前的研究中提出了用于消除单麦克风声学回声和背景噪声的频谱增强技术，但去混响方面的研究仍待深入。" 在声学回声消除（AEC）领域，非线性方法被广泛应用于处理复杂环境中的回声问题。传统的线性滤波器如最小均方误差（LMS）算法虽然能够有效处理某些情况下的回声，但在存在强混响和噪声的环境中，它们的表现往往受限。因此，非线性方法应运而生，其目标是更精确地分离近端语音和各种干扰信号。论文中提到的联合去混响和残余回声抑制策略旨在同时解决这两个问题。去混响是指去除由声源引起的室内反射造成的延迟和衰减效应，而残余回声抑制则关注于即使经过基本的AEC处理后仍存在的回声成分。这种联合处理方法通常基于更复杂的统计模型，例如利用统计建模或机器学习技术来识别并消除这些干扰。具体来说，可能采用的方法包括但不限于： 1. **频谱增强技术**：通过分析声音信号的频谱特性，针对性地削弱或消除混响和噪声频段，同时保护语音的质量。 2. **自适应滤波器**：利用自适应算法不断调整滤波器系数，以适应变化的环境条件和语音特性，从而更有效地消除回声。 3. **非负矩阵分解（NMF）**：这是一种非线性表示学习方法，可用于分解信号，将语音和噪声成分分离。 4. **深度学习模型**：近年来，深度神经网络（DNN）和卷积神经网络（CNN）等深度学习方法已被应用于声学回声消除和去混响，通过大量的训练数据学习复杂的信号模式，以提高处理效果。 5. **盲信号分离技术**：如独立成分分析（ICA），尝试从混合信号中恢复出原始的独立源，以此来分离语音和噪声。 6. **时频分析**：利用短时傅立叶变换（STFT）或小波变换等时频分析工具，对信号进行局部处理，以捕捉瞬态的回声和混响特性。综合以上技术，论文可能会提出一种创新的框架，将非线性处理与去混响和回声抑制相结合，以适应会议系统等实际应用场景。这样的方法可以显著改善音频通信质量，特别是在高混响和噪声的环境中，对提升语音清晰度和可理解性具有重要意义。

1436 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 16, NO. 8, NOVEMBER 2008

path usually contribute to speech intelligibility. Therefore, we

focus on the estimation of the early speech component

The observed microphone signal

can be written as

(5)

Using (4) and (5) the error signal

can be written as

(6)

Using the short-time fourier transform (STFT), we have in the

time–frequency domain

(7)

where

represents the frequency bin and the time frame. In

the next section, we show how the spectral component

can be estimated.

III. G

ENERALIZED POSTFILTER

In this section, the postﬁlter is developed that is used to

jointly suppress late reverberation, residual echo, and back-

ground noise. When residual echo and noise are suppressed,

Gustafsson et al. [30] and Jeannès et al. [11] concluded that

the best result is obtained by suppressing both interferences

together after the AEC. The main advantage of this approach

is that the residual echo and noise suppression does not suffer

from the existence of a strong acoustic echo component. Fur-

thermore, the AEC does not suffer from the time-varying noise

suppression. A disadvantage is that the input signal of the

AEC has a low signal-to-noise ratio (SNR). To overcome this

problem, algorithms have been proposed where, besides the

joint suppression, a noise-reduced signal is used to adapt the

echo canceller [31].

Here, a modiﬁed version of the OM-LSA estimator [32] is

used to obtain an estimate of the spectral component

Given two hypotheses,

and , which indicate,

early speech absence and early speech presence, respectively,

we have

Let us deﬁne the spectral variance of the early speech compo-

nent, the late reverberant speech component, the residual echo

signal, and the background noise, as

, and , re-

spectively. The a posteriori SIR is then deﬁned as

(8)

and the a priori SIR is deﬁned as

(9)

The spectral variance

of the background noise

can be estimated directly from the error signal , e.g., by

using the method proposed by Martin in [33] or by using the

improved minima controlled recursive averaging (IMCRA) al-

gorithm proposed by Cohen [34]. The latter method was used

in our experimental study. The spectral variance estimators for

and are derived in Sections IV and V, respec-

tively. The a priori SIR cannot be calculated directly since the

spectral variance

is unobservable. Different estimators

can be used to estimate the a priori SIR, e.g., the decision direct

estimator developed by Ephraim and Malah [35] or the recursive

causal or noncausal estimators developed by Cohen [36]. In the

sequel, the decision directed estimator is used for the estima-

tion of the a priori SIR. The decision directed-based estimator

is given by [35]

(10)

where

is the instantaneous SIR

(11)

and

is a lower-bound on the a priori SIR that helps to

reduce the amount of musical noise. The weighting factor

controls the tradeoff between the amount of

noise reduction and transient distortion introduced into the

signal. The weighting factor is commonly chosen close to one,

e.g.,

. A larger value of results in a greater reduction

of musical noise, but at the expense of attenuated speech onsets

and audible modiﬁcations of transient components. Although

(10) can be used to calculate the total a priori SIR, it does not

allow to make different tradeoffs for each interference. One can

gain more control over the estimation of the a priori SIR by

estimating it separately for each interference. More information

regarding this and combining the separate a priori SIRs can be

found in Appendix A.

When the early speech component

is assumed to be

active, i.e.,

is assumed to be true, the log spectral am-

plitude (LSA) gain function is used. Under the assumption that

and the interference signals are mutually uncorrelated, the

LSA gain function is given by [14]

(12)

where

(13)

When the early speech component

is assumed to be in-

active, i.e.,

is assumed to be true, a lower-bound

is applied. In many cases, the lower-bound

is used, where speciﬁes the maximum

amount of interference reduction. To avoid speech distortions

is usually set between and dB. However, in

practice the residual echo and late reverberation needs to be

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