现代通信中的语音处理：挑战与前景

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"语音处理在现代通信中的挑战与前景" 随着人类与人类、人类与机器之间的通信设备日益增多，声音拾取和呈现成为必需，这使得复杂的算法变得至关重要。我们所处的声学环境极具挑战性，这正是需要高级语音处理技术的原因。SpringerTopics in Signal Processing系列书籍专门探讨了这一领域，由J. Benesty和W. Kellermann主编，其中第三卷《语音处理在现代通信》重点关注了现代通信中语音处理的挑战和未来视角。该书由Israel Cohen、Jacob Benesty和Sharon Gannot等人编辑，他们都是在信号处理和语音技术领域的专家。书中可能涵盖了以下关键知识点： 1. **麦克风阵列信号处理**：在Vol.1中，详细讨论了如何利用麦克风阵列技术来提高声音捕获的性能，包括噪声抑制、声源定位和波束形成等技术。 2. **噪声抑制**：Vol.2深入研究了在语音处理中的噪声减少方法，如自适应滤波器、谱减法和基于深度学习的降噪技术，这些技术有助于在复杂环境下提高语音清晰度。 3. **语音处理基础**：在Vol.3中，可能涵盖了语音信号的数字化、特征提取（如MFCC）、语音识别和合成的基本原理，这些都是现代通信系统的核心组成部分。 4. **现代通信挑战**：书中可能会分析现代通信系统中面临的挑战，例如无线通信环境下的多径传播、背景噪声干扰以及实时处理需求。 5. **先进算法应用**：可能讨论了最新的信号处理算法，如深度学习在语音识别和增强中的应用，以及如何利用这些算法改善通信质量。 6. **人机交互**：在人机通信中，理解并适应人类语言的自然性和复杂性是一项重大挑战。书里可能包含了语音识别、语音命令控制和自然语言处理等相关技术。 7. **实际应用案例**：书中可能包含各种实际应用示例，如智能助手、电话会议系统、听力辅助设备和自动驾驶汽车的语音交互系统等。 8. **未来展望**：最后，作者们可能会对未来的研究方向和潜在技术进步进行预测，比如5G和6G网络对语音通信的影响，以及物联网(IoT)和边缘计算对语音处理技术的需求。《语音处理在现代通信》一书深入探讨了如何在日益复杂的环境中优化语音通信，结合了理论与实践，为相关领域的研究人员和工程师提供了宝贵的参考资料。通过理解和应用这些知识点，可以推动语音技术在现代通信系统中的进一步发展。

List of Contributors

Ari Abramson

Technion–Israel Institute of Technology, Israel

e-mail: aari@tx.technion.ac.il

Morag Agmon

Ben-Gurion University of the Negev, Israel

e-mail: moraga@ee.bgu.ac.il

Yekutiel Avargel

Technion–Israel Institute of Technology, Israel

e-mail: kutiav@tx.technion.ac.il

Jacob Benesty

INRS-EMT, QC, Canada

e-mail: benesty@emt.inrs.ca

Rapha¨el Blouet

Audionamix, France

e-mail: raphael.blouet@audionamix.com

Silviu Ciochin˘a

University Politehnica of Bucharest, Romania

e-mail: silviu@comm.pub.ro

Israel Cohen

Technion–Israel Institute of Technology, Israel

e-mail: icohen@ee.technion.ac.il

Jacek P. Dmochowski

City College of New York, NY, USA

e-mail: jdmochowski@ccny.cuny.edu

xvii

2 Y.AvargelandI.Cohen

1.1 Introduction

Identiﬁcation of linear systems has been studied extensively and is of major

importance in diverse ﬁelds of signal processing, including acoustic echo can-

cellation [1, 2, 3], relative transfer function (RTF) identiﬁcation [4], derever-

beration [5, 6], blind source separation [7, 8] and beamforming in reverberant

environments [9, 10]. In acoustic echo cancellation applications, for instance,

a loudspeaker-enclosure-microphone (LEM) system needs to be identiﬁed in

order to reduce the coupling between loudspeakers and microphones. Tradi-

tionally, the identiﬁcation process has been carried out in the time domain

using batch or adaptive methods [11]. However, when long-memory systems

are considered, these methods may suﬀer from slow convergence and ex-

tremely high computational cost. Moreover, when the input signal is corre-

lated, which is often the case in acoustic echo cancellation applications, the

estimation process may lead to high estimation-error variance and to slow

convergence of the adaptive algorithm [12].

To overcome these problems, block processing techniques have been in-

troduced [13, 12]. These techniques partition the input data into blocks and

perform the adaptation in the frequency domain to achieve computational

eﬃciency. However, block processing introduces a delay in the signal paths

and reduces the time-resolution required for control purposes. Alternatively,

subband (multirate) techniques [14] have been proposed for improved system

identiﬁcation (e.g., [15, 16, 17, 18, 19, 20, 21]). Accordingly, the desired sig-

nals are ﬁltered into subbands, then decimated and processed in distinct sub-

bands. Some time-frequency representations, such as the short-time Fourier

transform (STFT), are employed for the implementation of subband ﬁltering

(see Fig. 1.1) [22, 23, 24].

The main motivation for subband methods is the reduction in computa-

tional cost compared to time-domain methods, due to processing in distinct

subbands. Together with a reduction in the spectral dynamic range of the

input signal, the reduced complexity may also lead to a faster convergence of

adaptive algorithms. Nonetheless, because of the decimation, subband tech-

niques produce aliasing eﬀects, which necessitates crossband ﬁlters between

the subbands [19, 25]. It has been found [19] that the convergence rate of

subband adaptive algorithms that involve crossband ﬁlters with critical sam-

pling is worse than that of fullband adaptive ﬁlters. Several techniques to

avoid crossband ﬁlters have been proposed, such as inserting spectral gaps be-

tween the subbands [15], employing auxiliary subbands [18], using polyphase

decomposition of the ﬁlter [20] and oversampling of the ﬁlter-bank outputs

[16, 17]. Spectral gaps impair the subjective quality and are especially an-

noying when the number of subbands is large, while the other approaches are

costly in terms of computational complexity.

A widely-used approach to avoid the crossband ﬁlters is to approximate

the transfer function as multiplicative in the STFT domain [26]. This ap-

proximation relies on the assumption that the support of the STFT analysis

1 Linear System Identiﬁcation in the STFT Domain 3

window is suﬃciently large compared with the duration of the system impulse

response. As the length of the analysis window increases, the multiplicative

transfer function (MTF) approximation becomes more accurate. Due to its

computational eﬃciency, the MTF approximation is useful in many real-world

applications (e.g., [27, 24, 4, 9]). However, since such applications employ ﬁ-

nite length windows, the MTF approximation is never accurate. Recently,

the cross-MTF (CMTF) approximation, which extends the MTF approxima-

tion by including cross-terms between subbands, was introduced [28]. This

approach substantially improves the steady-state mean-squared error (mse)

achieved by the MTF approximation, but suﬀers from slower convergence.

In this chapter, we discuss three diﬀerent models for linear system identi-

ﬁcation in the STFT domain. We start by considering the inﬂuence of cross-

band ﬁlters on the performance of a system identiﬁer implemented in the

STFT domain [25]. We introduce an oﬄine estimation scheme, and derive

analytical relations between the input signal-to-noise ratio (SNR), the length

of the input signal, and the number of crossband ﬁlters which are useful for

system identiﬁcation. We show that increasing the number of crossband ﬁl-

ters not necessarily implies a lower mse in subbands. As the power of input

signal increases or as the time variations in the system become slower, a

larger number of crossband ﬁlters should be estimated to achieve the mini-

mal mse (mmse). We proceed with investigating the inﬂuence of the STFT

analysis window length on the performance of a system identiﬁer that utilizes

the MTF approximation [26]. The MTF in each frequency bin is estimated

oﬄine using a least squares (LS) criterion. We show that the performance

does not necessarily improve by increasing the window length. The optimal

window length, which achieves the mmse, depends on the SNR and the input

signal length. Finally, we introduce the CMTF model which includes cross-

terms between distinct subbands for improved system identiﬁcation [28]. We

propose an algorithm that adaptively controls the number of cross-terms to

achieve the mmse at each iteration [29]. The resulting algorithm achieves a

substantial improvement in steady-state performance over the conventional

MTF approach, without compromising for slower convergence. Experimental

results with white Gaussian signals and real speech signals validate the the-

oretical results derived in this chapter and demonstrate the eﬀectiveness of

the proposed approaches for subband system identiﬁcation.

The chapter is organized as follows. In Section 1.2, we address the problem

of system identiﬁcation in the time and STFT domains. In Section 1.3, we in-

troduce the crossband ﬁlters representation for linear systems, and investigate

the inﬂuence of crossband ﬁlters on the performance of a system identiﬁer

implemented in the STFT domain. In Section 1.4, we present the MTF ap-

proximation and investigate the inﬂuence of the analysis window length on

the mse performance. In Section 1.5, we introduce the CMTF model and

propose an adaptive-control algorithm for estimating the model parameters.

Finally, in Section 1.6, we present experimental results which demonstrate

the proposed approaches to subband system identiﬁcation.

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