An Array of First Order Di erential Microphone Strategies for … · 2019-07-14 · Master Thesis...

Master ThesisElectrical engineeringThesis no: MSE-20YY-NNMM YYYY

An Array of First OrderDifferential Microphone Strategiesfor Enhancement of Speech Signals

Naresh Reddy. NagiReddyArun Kumar. Korva

School of Engineering

Blekinge Institute of Technology

SE-371 79 Karlskrona

Sweden

This thesis is submitted to the School of Engineering at Blekinge Institute of Technology

in partial fulfillment of the requirements for the degree of Master of Science in Electrical

Engineering with emphasis on Telecommunications.

Contact Information:Authors:Naresh Reddy. NagiReddy 850706-1698E-mail: [email protected]

Arun Kumar. Korva 830627-2819E-mail: [email protected]

University advisor:Dr. Nedelko GrbicSchool of Engineering (ING)E-mail: [email protected]: +46 455 38 57 27

School of EngineeringBlekinge Institute of Technology Internet : www.bth.se/comSE-371 79 Karlskrona Phone : +46 455 38 50 00Sweden Fax : +46 455 38 50 57

Abstract

The quality and intelligibility of speech is degraded with the presence of background noisein the speech signal, which affects the listeners ability to understand the speech clearly.Speech enhancement is the process with which the background noise can be suppressedto improve the quality and intelligibility of the speech signal. With the development ofspeech based human computer interfaces, the demand for speech enhancement is growing.There are many applications like hand free mobile communication, teleconferencing,automatic speech recognition, hearing aids etc where there is a requirement for speechenhancement due to the noise interruption in the speech signal.Among all the applications mentioned, hearing aids are the ones which drew attention.This motivated us to go in depth with the research on hearing aids using different speechenhancement techniques and algorithms to enhance the quality of the speech at theend user. There are many algorithms and techniques that can be used to enhance thespeech signals quality and intelligibility. Several beamforming techniques using multi-microphone arrays are widely used at present in the field of speech enhancement. In thisthesis, Elko and Wiener beamforming algorithms with first order differential microphonearrays are being used to enhance the speech signal in an application, especially likehearing aids.The main reason for using Elko algorithm is: it tracks and attenuates the backgroundnoise or interference present in the back half plane of the microphone array. The Wienerbeamformer is used as; it is a minimum mean square error beamformer which has theability to nullify all the interference signals and sustains a high level of performance bygetting signals from the desired direction. In this thesis, the assemblage of Elko andWiener beamformers is also implemented as the Elko-Wiener Beamformer.These algorithms were implemented in a computer simulated anechoic chamber usingMATLAB R2008. Recorded male and female speech signals sampled at 16 KHz wereused as inputs to the system, where female speech is the target signal vocalizing fromforward direction to the microphone array and male speech is the interference signalimpinging from the backward direction to the microphone array.The performance metrics used to measure the quality of the speech signal are signal-to-noise ratio increment (SNRI), speech and noise distortions and ITU-T recommendedPESQ MOS values. The simulation results show that the Elko-Wiener Beamformer hasthe advantages of individual Elko and Wiener Beamformers giving 29dB SNRI. TheElko and the Wiener Beamformers has 10 dB and 17 dB SNRI respectively. Hence theElko-Wiener joint Beamformer outperforms its individual beamformers in its class.

Key words: speech enhancement, beamforming, microphone array, elko algorithm,wiener beamformer, differential microphone.

i

Acknowledgments

We would like to dedicate our thesis work to our family members. We have ourgreat respect and sincere gratitude for their support, love and encouragement.

It is a great honor and privilege to thank our honorable thesis supervisor Dr.Nedelko Grbic at, ING School of Engineering, Blekinge Institute of Technology,who guided our work with his scholarly advice. Without his guidance and en-couragement, this task would have been unachievable. We express our heartfeltgratitude to him.

We are thankful to our friend LeelaKrishna.G who has been helping and guid-ing us in every aspect of our thesis work. We would like to thank our friends whosupported us during our master’s thesis.

ii

To Our Parents

iii

List of Figures

1.1 Path of speech from position P to L in a closed room . . . . . . . 2

2.1 Cardioid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Super-cardioid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Hyper-cardioid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.4 Bidirectional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.5 Shotgun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.6 Omnidirectional . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 Working of beamformer . . . . . . . . . . . . . . . . . . . . . . . . 8

4.1 First order sensor containing two zero-order sensors and a delay . 134.2 Directional responses for the two-element microphone array (a)T=0,

(b)T=(d/c)/2 (c)T=(d/c) . . . . . . . . . . . . . . . . . . . . . . 144.3 First order differential microphone using back-to-back cardioid sys-

tem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.4 First order sensor containing two zero-order sensors and a delay . 15

5.1 Matrices for wiener beamformer (required and interferenced) [13] . 175.2 Diagrammatic representation of process . . . . . . . . . . . . . . . 185.3 Wiener beamformer structure . . . . . . . . . . . . . . . . . . . . 19

7.1 Simulation set-up in anechoic chamber . . . . . . . . . . . . . . . 237.2 Elko Beamformer Simulation Set-up. . . . . . . . . . . . . . . . . 247.3 Signal plots representing degraded and enhanced Signals of Elko

Beamformer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247.4 Graph representing input and output SNRs of Elko Beamformer . 257.5 Graph representing input and output PESQ MOS values of Elko

Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267.6 Graph representing the Speech Distortion curves of Elko Beam-

former at 0dB input SNR . . . . . . . . . . . . . . . . . . . . . . 267.7 Graph representing noise distortion curves of Elko Beamformer at

0dB input SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277.8 The wiener beamformer simulation set-up . . . . . . . . . . . . . 28

iv

7.9 Signal plots representing degraded and enhanced Signals of WienerBeamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

7.10 Graph representing input and output SNRs of wiener beamformer 297.11 Graph representing speech distortion curves of the wiener beam-

former at 0dB input SNR . . . . . . . . . . . . . . . . . . . . . . 307.12 Graph representing noise distortion curves of wiener beamformer

at 0dB input SNR . . . . . . . . . . . . . . . . . . . . . . . . . . 307.13 Graph representing input and output PESQ MOS values of wiener

Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317.14 Elko Wiener Beamformer simulation setup . . . . . . . . . . . . . 327.15 Elko-Wiener Signal Plots representing degraded and enhanced signals 327.16 Graph representing input and output SNRs of Elko-Wiener Beam-

former . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337.17 Graph representing the Speech Distortion curves of Elko-Wiener

Beamformer at 0dB input SNR . . . . . . . . . . . . . . . . . . . 347.18 Graph representing the Noise Distortion curves of Elko-Wiener

Beamformer at 0dB input SNR . . . . . . . . . . . . . . . . . . . 347.19 Graph representing input and output PESQ MOS values of Elko-

Wiener Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . 357.20 Input vs. output SNRs comparison plot . . . . . . . . . . . . . . . 367.21 SNR 3D comparison plot . . . . . . . . . . . . . . . . . . . . . . 377.22 PESQ MOS comparison plot . . . . . . . . . . . . . . . . . . . . . 37

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List of Tables

6.1 Quality option score used in PESQ . . . . . . . . . . . . . . . . . 22

7.1 Table showing results of elko beamformer under different inputSNR levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

7.2 Table showing results of wiener beamformer under different inputSNR levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

7.3 Table showing results of elko-wiener beamformer under differentinput SNR levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

vi

Contents

Abstract i

Acknowledgments ii

1 Introduction 11.1 Objectives of study . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Structure of report . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Differential microphones 42.1 Microphones characteristics . . . . . . . . . . . . . . . . . . . . . 42.2 Microphone arrays . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Microphone polar pattern . . . . . . . . . . . . . . . . . . . . . . 6

3 Beamformer 83.1 Beamformer introduction and classifications . . . . . . . . . . . . 83.2 Fractional Delays . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Elko beamformer 124.1 Elko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.1.1 Derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5 Wiener beamformer 165.1 Wiener beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.1.1 Wiener beamformer (WBF) . . . . . . . . . . . . . . . . . 17

6 Evaluation metrics 206.1 Signal to Noise ratio . . . . . . . . . . . . . . . . . . . . . . . . . 206.2 Speech distortion normalized . . . . . . . . . . . . . . . . . . . . . 216.3 Noise distortion normalized . . . . . . . . . . . . . . . . . . . . . 216.4 Perceptual evaluation of speech quality (PESQ) . . . . . . . . . . 216.5 Evaluation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 22

7 Results 237.1 The Elko Beamformer . . . . . . . . . . . . . . . . . . . . . . . . 247.2 The Wiener Beamformer . . . . . . . . . . . . . . . . . . . . . . . 28

vii

7.3 Elko Wiener Beamformer . . . . . . . . . . . . . . . . . . . . . . . 327.4 Results Comparison Plots . . . . . . . . . . . . . . . . . . . . . . 36

8 Summary and Conclusion 38

9 Future Work 40

viii

Chapter 1

Introduction

Research shows that 1.3 million persons have slight hearing impairment (agegroup of 18 and above). In addition to these 495,000 have moderate hearingimpairment and 120,000 have a very severe hearing impairment [14]. When theenvironment becomes too noisy or reverberant using monaural hearing aids makesdifficult for impaired listeners to understand speech easily [18]. Since hearingimpairment reduces both binaural, monaural phenomenon, using hearing aids onboth the ears to simulate “cocktail-party effect” (phenomenon of hearing withtwo-ears) fails to normalize listening among impaired listeners. However, hearingaids help to reduce difficulty in hearing but they are not a complete substituteand not everyone is benefited from using hearing aids. In general terms hearingaids is necessary for severely impaired listeners, in case of persons with moderatehearing impairment, hearing aids can improve speech intelligibility.

The purpose of this study is to improve the efficiency of existing hearingaids used by impaired listeners. There are various properties and constraints,which influence working of hearing aids. Following are few important entitiesthat contribute to the working of hearing aids:

Properties of sound in an environment:

To understand the properties of sound let us consider these properties ofsound in a closed environment such as a room. When a person speaks ina closed environment such as a room, speech reaches listener directly. Thisspeech does not only travel directly but the surrounding walls can also reflectit. To understand this concept further, take a look at the fig 1.1. This figureshows different paths that speech takes from a position P (Speaker) to L(Listener). The first bar shows a direct path and other paths are reflections.These reflections take longer distance to reach listener, than direct paths.Based on the path, direction and speed of these speech impulses, there issome variation in how speech arrives listener’s ears. Based on this figure andthe arrival time following graph can be drawn. This graph shows variationin arrival time at listener’s ears. Path length and coefficient of absorption(reflecting efficiency of surrounding walls) collectively help to calculate theamplitude of a particular reflection. This coefficient of absorption variesfrom 0 to 1. Here 1 represents total absorption and 0 total reflection [18].

1

Introduction

Figure 1.1: Path of speech from position P to L in a closed room

Any reflection that reaches the listener after 40 milliseconds of receiving thesound from direct path it might be heard as a distinct echo. Reflectionsthat arrive 20 to 40 milliseconds of receiving direct path sound can createconfusion and cause difficulty in understanding the speech.

Acceptable levels sound:

Human ears are sensitive and can respond to remarkable range of sounds.But sounds that are between 130 db to 140 db are painful to human ears [10].Differences in sounds loudness are observed from 3 db at the lower thresholdand 0.5 db for loud sounds. In addition to this a sound is considered to behigh or low in a logarithmic pattern, where a ten fold increase in soundpower is described as twice as loud.

1.1 Objectives of study

From the above discussion it can be understood that human ears are sensitive toloud noises and at the same time there are some criteria, which should be takeninto consideration while designing a hearing aid. It can also be made clear thatsimulating a normal human ear using hearing aids has not yet been successful.

This thesis further aims to find out possibilities in designing a better hearingaid using an array of microphones and beamformer algorithms (Wiener and Gary-Elko).

2

Introduction

1.2 Structure of report

Chapter 1: Introduction, on need for hearing aids, environment of hearing aidsand objective of this study.

Chapter 2: Differential microphones

Chapter 3: Beamformer and fraction delays

Chapter 4: Gary-Elko beamformer

Chapter 5: Wiener beamformer

Chapter 6: Evaluation metrics

Chapter 7: Results, outcomes of the experiment conducted under controlledcircumstances

Chapter 8: Summary and Conclusion

Chapter 9: Future work

3

Chapter 2

Differential microphones

Hearing aid is combination of microphones arranged in a specific pattern andalso a combination of few algorithms, which can analyze the signal received atthe microphones. The properties and types of microphones are explained in detailunder this chapter.

2.1 Microphones characteristics

Microphones are an acoustic-to-electric transducer or sensor, which converts speechinto electrical impulses. Microphones use electro magnetic induction, capacitancechange, piezoelectric generation, or light modulation to convert mechanical vi-bration (i.e., pressure exerted by speech) into electrical signals, which are furtherprocessed. There are different varieties of microphones based on transducer prin-ciple such as condenser, dynamic etc. There are various other properties thatcontribute to selection of right set of microphones. They are as follows:

Sensitivity:

The microphone converts pressure exerted by speech into electrical signals.The extent to which a microphone can convert this pressure into intenseelectrical signals can be termed as sensitivity. Microphones sensitivity isimportant because, if a microphone is less sensitive then its output cannotbe processed by certain applications. Similarly if the microphone is toosensitive it might produce lot of distortion in the output sound.

Over load characteristics:

Microphones when given loud sounds to process, then such sounds over-load the input electronics and create distortions. When a microphone iscontinuously exposed to loud sound constantly, it can cause damage to themicrophone diaphragm. If the diaphragm is damaged the microphone mightlose sensitivity to process the normal sounds also.

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Linearity or distortion:

The distortion characteristic in a microphone varies in the way their di-aphragm is designed and assembled. Premium quality headphones arethose, which produce accurate electrical impulses with minimum distor-tion rate. The difference in model numbers is due to variation in quality ofoutput these microphones produce.

Noise:

Microphones should be built in such a way that every part in it is carefullyaligned. Since electrical impulse from microphones are very small, even ifthere is a slight disturbance that can make a huge difference to the soundwhen it is amplified. So slightest detail in building a microphone must bekept into consideration while selecting it for use.

2.2 Microphone arrays

Microphones are positioned in an array to capture spatial information easily.When these microphones are used in arrays in signal processing, it can help inestimating some parameters or extraction of some signals. The extraction ofsignals depend on the application and spatio-temporal information available atoutput of microphone [6]. Depending on the type of application, microphonesare arranged. If this arrangement is done application specific it can help informulation of processing algorithms. Microphone array processing algorithms aremany, some of them are generated and some others are borrowed from narrowband array processing. The advantage of borrowing algorithms available withantenna arrays is that these can be extended without much effort [16]. Butthe issue is that these algorithms are not made to work in the real acousticenvironments.

These microphone arrays when used properly help to solve many problemssuch as:

• noise reduction,

• echo reduction,

• dereverberation,

• localization of a single source,

• estimation of the number of sources,

• localization of multiple sources,

• source separation, and

5


• cocktail party.

When sounds received at microphones are passed through filters the aboveproblems can be reduced.

For example in a hands-free devices, signals received are not only those re-ceived from a direct path, but there are some other signals that are received fromdelayed replicas of original signal. These delayed replicas are received due toreflections from boundaries and objects in a room. This kind of reflected sig-nals that are received at microphones end are due to reflections and diversion inspeech signals and are termed as reverberations. To improve the intelligibility ofspeech signals de-reverberation is required. This improvement is possible whenreverberation can be identified.

In acoustic environments and applications such as automatic camera tracking,beamformer steering for reducing noise and reverberations, identifying sourcelocations can be helpful. The problem of identifying source is often called assource-localization. Positioning microphone arrays in 2 or 3 dimensions can helpin finding arrival angle or cartesian coordinates of a source. In addition to thismicrophone arrays can also help separate signals coming from same or differentdirections and same or different source.

In some situations using multiple arrays of microphones or multiple micro-phones in an array can increase possibility of solving this estimation problem[5].

2.3 Microphone polar pattern

Microphones have sensitivity to receiving speech signals, based on the directionof this sensitivity to speech signals microphones directionality or polar patternsis decided. Following are some of the polar patterns [3]:

Omnidirectional: This type of microphones response is a perfect sphere in 3 di-mensions. The polar pattern of an omnidirectional microphone is functionof frequency. Microphones with smallest diameter exhibit omnidirectionalcharacteristics at high frequency. Since these microphones do not employresonant cavities as delays, these microphones can be called the purest mi-crophones.

Bi-directional: Sound reaches equally at front and back in this kind of micro-phones. Sounds received from sides do not influence these microphones,because these sounds reach both front and rear side of microphone at sametime, which causes no gradient. These microphones respond to gradientalong the axes normal to the plane of diaphragm.

Shotgun: These microphones are highly directional. Sensitivity of this micro-phone is concentrated towards front and it has small areas of sensitivity on

6


Figure 2.1: Cardioid Figure 2.2: Super-cardioid

Figure 2.3: Hyper-cardioid

Figure 2.4: Bidirec-tional

Figure 2.5: Shotgun Figure 2.6: Omnidirec-tional

rear, left and right. These microphones are commonly used in televisionand film sets and for field recording in wildlife.

Unidirectional: A unidirectional microphone is sensitive to sounds from onlyone direction. The most common unidirectional microphone is Cardioidmicrophone.

Cardioid: This microphone is named cardioid because its sensitivity pattern isheart shaped. There are two other types under this category. They are,hyper-cardioid and super-cardioid. The hyper-cardioid microphone has asmaller rear sensitivity and tighter front sensitivity, but the difference in caseof super-cardioid microphone is that there is slightly lesser rear sensitivityand slightly more sensitivity in forward direction. These three are generallyused as speech microphones.

7

Chapter 3

Beamformer

3.1 Beamformer introduction and classifications

There is occurrence of noise and interfering signals in spatially propagating sig-nals. If the actual signal and interference signals occupy same temporal frequency,then temporal filtering cannot help separating actual signal from the interferers.But in general the interferer signal and original signal originate from a differentspatial locations. This spatial separation can be used to identify direct signalfrom interferers signal using a beamformer [2]. Beamformer has an array of sen-sors arranged in a specific pattern. Each of these sensors is fixed with filtersand the final filtered output signal is the resultant obtained after summation ofeach of these individual sensors output. These sensors can help synthesize largeaperture when a low frequency signal is used, which is not quite possible with asingle physical antenna. Another advantage is that, using array of sensors spatialfiltering versatility is offered by discrete sampling [1].

Working of beamformer

As described above beamformer is an arrangement of sensors and each of thesesensors are multiplied with certain weight and summed up to get the final outputsignal. A diagrammatical representation of this process is given in fig 3.1 Inthe above figure sound (speech) is sensed on left side of arrangement and thencalculated based on weights to give the output signal on the right side. Thisrepresentation is a more generalized form of a beamformer. The weights can be

Figure 3.1: Working of beamformer

8

Beamformer

fixed weights or adaptive weights. If weights dont vary they are fixed weights.But in some cases if the weights vary based on the position of sensors and variousother conditions then they are adaptive weights. Based on how weights are chosenbeamformers are further categorized as:

• Statistically independent

• Data independent

Statistically optimum beamformer

In statistically optimum beamformer weights are chosen based on the statisticsof array data and in case of data independent beamformer selection of weightsdoes not depend on array data. Statistics of this data keeps varying over timeso adaptive algorithms are used to calculate weights. Adaptive algorithms aredesigned so that beamformer responses come to a statistically optimum solution.

Data independent beamformer

In a data independent beamformer, weights are arranged such that beamformercan estimate desired response without depending on array data or data statistics.An example to this data independent beamformer is Delay and sum beamformer.

When weights are chosen statistically from the data received at the arraysthen that is called as statistically optimum beamformer. The output of throughthis beamformer consists of very few noise and signals that are arriving from otherthan the desired directions. An example of this statistically optimum beamformeris Frost beamformer.

Application of beamformer is in SONAR, RADAR, communications, imaging,geophysical exploration, Biomedical and also in acoustic source localization [19].

3.2 Fractional Delays

Fractional delay (FD) filters help in fine-tuning the sampling instances. The ap-plication of this is in many areas such as audio, communication, music technology,speech coding and synthesis, etc. [24]. This FD filters are used in some of theseapplications because actual samplings instances are also important in additionto the sampling frequencies. For any sample to represent the continuous originalsignal, it is required that the sample rate satisfy Nyquist criterion. In addition tosatisfying Nyquist criterion, it is required that the sampling instances should alsobe properly selected. The examples where this selection criterion is importantare in case of digital communications and in solving problems with modeling ofmusical instruments. Fractional delay is defined as “assuming uniform sampling,a delay that is a non-integer multiple of the sample interval” [24, 15, 23, 9, 25].

9

Beamformer

There are two known types of filters for approximating a fractional delay valuethey are designed in a non-recursive and recursive fashion. FIR come under non-recursive category and IIR, allpass filters come under recursive. This thesis uses‘Thiran’ allpass filter. Allpass filter is used as its magnitude of response is ex-actly unity at all frequencies and is well suited for fractional delay approximations.Other known allpass filters are as follows [15]:

1. Least squaes (LS) phase approximation;

2. LS phase delay approximation;

3. Maximally-flat group delay approx. (thiran allpass filter)

4. Iterative WLS phase error design (enables almost equiripple phase approx-imation

5. Iterative WLS phase error design (enables almost equiripple phase-delayapproximation)

Allpass filters are used in this context as they have some advantages over FIRfilters. A comparison between allpass filter and FIR show these advantages [22].

• Comparison in terms of frequency response error (FRE) magnitude showsthat the order of allpass filter is 5 and length of approximations in case ofFIR is 10 (these results are based on a wideband specification where 80%Nyquist limit is the passband of approximation).

• When a window function with 35-dB ripple level was selected for sinc win-dowing and a low pass (FIR FD) filter was considered the coefficients hadto be scaled to obtain best approximation, but when allpass filters wereconsidered they automatically scaled by the algorithms that were designed.

• There is difference in the range of delay D between FIR and allpass filterstoo.

• Error curves are symmetric in allpass filters and in case of thiran interpo-lation there is a lower limit for the delay to be approximated and is stableonly for D > N − 1.

• The Thiran allpass filter gives low performance in terms of peak FRE butit is easy to design and better suits for a narrow-band approximations [? ].

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Beamformer

The Thiran all-pole filter is used to maximally flat allpass FD filter. It canalso be noticed that even when the order is increased, there is not much decreasein error identified. The sample MATLAB code used in this thesis is as follows:function [A,B] = thiran(D,N)% [A,B] = thiran(D,N)% returns the order N Thiran allpass interpolation filter% for delay D (samples).A = zeros(1, N + 1);for k = 0:N

Ak = 1;for n = 0 : N

Ak = Ak ∗ (D −N + n)/(D −N + k + n);end

A(k + 1) = (−1) ∧ k ∗ nchoosek(N, k) ∗ Ak;end

B = A(N+1:-1:1);

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Chapter 4

Elko beamformer

4.1 Elko

One of the basic problem with acoustic transduction is detrimental effect of back-ground noise and as these communication devices are starting to become moreportable, the acoustic pickup by microphones need to be designed such that theyinclude a combination of mini transducers and signal-processing which can allowhigh quality communication. Reverberation is another issue that can have a neg-ative impact on reception quality in hands-free applications. To solve these issuesdirectional microphones can be used and in case of teleconferencing and personalcommunications differential microphone array is more suited. The differentialarray of microphones uses sensors, which are placed very closely when comparedto acoustic wavelength. Since these microphones are closely spaced and arrangedin a alternating sign fashion, this can help to realize directionality. As a resultthis arrangement gives a scope to produce increased directivity, which is higherthan the summed output of uniformly arranged sensor elements.

Using this ‘Elko’ suggested a different method for adaptive directional micro-phones [8, 7, 20, 21]. This suggested method includes implementation and designof adaptive first-order differential microphone that reduces microphone outputpower (constraint is first-order microphone null is located in rear-half plane).When this proposed method is used in some acoustic fields, there is improve-ment observed in signal-to-noise ratio. The idea behind this design is to proposean adaptive microphone system that adjusts its directivity based on speech sig-nals and maximizes signal-to-noise ratio. This adaptive differential microphoneis combination of two omnidirectional elements to form back-to-back cardioid di-rectional microphones. Weighted subtraction of these outputs helps to realizefirst-order array [8].

4.1.1 Derivation

Assume a sound wave of s(t) with a spectrum S(ω) is incident at an angle ‘θ’ on toa two-element microphone array, which are spaced at a distance ‘d’. Since thereis an angle of incidence to the two-element microphone array, sound will reach

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Elko beamformer

Figure 4.1: First order sensor containing two zero-order sensors and a delay

both these microphones with a time delay of τ . And this ‘τ ’ can be denoted as

τ =a

c=dcosθ

c(4.1)

In the above equation (4.1) ‘c’ is the speed with which sound propagates. Toachieve the final sound signal y(t), output of sound signal at microphone whichreceives the sound signal earlier to that of the other needs to be subtracted.

y(t) = s(t− τ)− s(t− T ) = s(t− cosθ

c)− s(t− T ) (4.2)

Frequency domain form to the equation (4.2) is

Y (ω, θ) = (e−jωdcosθ

c − e−jωT ) (4.3)

the directional response for arrangement shown in fig above (fig.4.1) is shownbelow figure (fig.4.2). Here we can observe that when delay is varied amongdifferent values of T (i.e. 0 and d

c) null location can be changed or steered from

90o to 0o

One way to work out in achieving adaptive directional microphone is to im-plement a changing time delay T on the above discussed array of microphones.For this to happen is feasibility to generate 0 ≤ T ≤ d

cand calculations for these

approximations in real-time are going to be numerous. From the figure it can beidentified that when a fixed time delay of one sample and when a time period ismaintained at d

c. This requires less computational cost even. So based on this a

back-to-back cardioid system can also be implemented, which can be done by thesetup described in fig.4.3 The output to this can be written as

CF (t) = s(t)− s(t− T − τ) = s(t)− s(t− d

c− dcosθ

c) (4.4)

CB(t) = s(t− τ)− s(t− T ) = s(t− dcosθ

c)− s(t− d

c) (4.5)

y(t) = CF (t)− β ∗ CB(t) (4.6)

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Elko beamformer

Figure 4.2: Directional responses for the two-element microphone array (a)T=0,(b)T=(d/c)/2 (c)T=(d/c)

Figure 4.3: First order differential microphone using back-to-back cardioid system

Frequency domain form to equation (4.6) is

Y (ω, θ) = CF (ω, θ)− β ∗ CB(ω, θ) (4.7)

Y (ω, θ) = S(ω)b1− e−jωdc(1+cosθ) − βe−jω

dccosθ + e−jω

dc c (4.8)

in the above equation time delay T is fixed and null can be varied between 180oand 90o by changing β from 0 to 1. The time delay maintained T=1 and bychanging values of β the directional responses shown in fig 4.4 are observed inthe system. To make the system adaptive further, LMS algorithm is used withback-to-back cardioid first order differential array. Differentiating and squaringequation (4.6) results in

dy2(t)

dβ= −2y(t)CB(t) (4.9)

The resultant LMS version of above equation is

βt+1 = βt + 2µy(t)CB(t) (4.10)

14

Elko beamformer

Figure 4.4: First order sensor containing two zero-order sensors and a delay

Normalizing the above equation gives us,

βt+1 = βt + 2µy(t)CB(t)

〈Cb2(t)〉(4.11)

Here, µ is the step size and 〈Cb2(t)〉 is the time to normalize µ.

15

Chapter 5

Wiener beamformer

5.1 Wiener beamformer

Wiener beamformer is also called as minimum mean square error beamformer [11].A detailed discussion on wiener beamformer is described in articles [11, 26, 12, 13]

Wopt = argminwEy[n]− sr[n]2r ∈ [1, 2, ...N ] (5.1)

Single reference mic observation when only required signal is selected as input isdenoted by Sr[n]. In the above equation y[n] is the output and it is the optimumoutput signal that is inferred from optimal weights.

y[n] =N∑i=0

L∑j=0

−1wi[j]x[n− j] (5.2)

The above equation is inferred from beamformer. Here L-1 is order of filter andw[j]. Where j varies from 0 to L-1 and these valuse are the filter taps for I th mic.xi(n) is the ith mic observation and N is microphone number.Given below is theequation to maximize mean square error between output and reference signal.

Wopt = [Rss +Rnn]−1rs (5.3)

In the above equation auto-correlation matrices for signal of interest and noiseare denoted by Rss and Rnn respectively.

Rss =

Rs1s1 Rs1s2 . . . Rs1sN

Rs2s1 Rs2s2 . . . Rs2sN

......

. . ....

RsNs1 RsNs2 . . . RsNsN

(5.4)

Rnn =

Rn1n1 Rn1n2 . . . Rn1nN

Rn2n1 Rn2n2 . . . RN2nN

......

. . ....

RnNn1 RnNn2 . . . RnNnN

(5.5)

16

Wiener beamformer

The cross correlation vector rs is denoted as

rs = [r1r2...rn] (5.6)

And each element in this is represented as,

ri[k] = Esi[n]sr ∗ [n+ k]i = 1, 2, 3, ....N, r ∈ [1, 2, ....N ], K = 0, 1, 2, ..., L− 1(5.7)

The autocorrelation matrix of the required signal Rss is designed around crosscorrelation vector rs. The optimal weights (w) are arranged as,

w = [wT1 wT2 . . . w

Tn ]T (5.8)

In the next chapter wiener beamformer is evaluated and results are discussed.

5.1.1 Wiener beamformer (WBF)

To get microphone response at each microphone, simulation procedure for wienerbeamforming is initiated before adding required and interference signal. For thisinterference and desired signal are considered separately (at each microphone)and represented in a matrix form. The order of required and interference ma-trices would be N x S, when ‘S’ is assumed to be the total number of speechsamples and interference signals at each microphone. Microphones response todesired/interference is represented by each row in the matrix.

Figure 5.1: Matrices for wiener beamformer (required and interferenced) [13]

As discussed earlier section, equation 13 represents the optimum weights thatmaximizes mean square error between reference signal and output. This requiresauto correlation matrices (for interference and required signals), correlation vec-tors. The method used to infer these matrices with the help of M S and M I isas follows [13]:

17

Wiener beamformer

1. Assuming the order of wiener filter to be 64, create an empty matrix withorder 64*N x K as X. Here, K is the integer part S/64.

2. In the next step pick first 64 columns from matrix M S and make theseselected elements into a N x 64 matrix into a single column vector andname it as x1. Now, substitute the first column of X with x1.

3. Similarly pick next 64 columns (65 to 128) from M S, convert it into columnvector, name it as x2 and substitute it in the second column of X. continuingthis process till end of M S and this will give matrix X. in the same waymatrix Y can be obtained from M I. Diagrammatic representation of thisprocess is shown in

Figure 5.2: Diagrammatic representation of process

From these auto-correlation matrices X and Y interference signal I(n) and requiredsignal s(n) can be calculated through the following equations: required (Rss) and

18

Wiener beamformer

interference signal (Rnn) can be represented in auto-correlation forms as follows:

Rss =1

K

∑i

= 1k(xi.xTi )Rnn =

1

K

∑i

= 1k(yi.yTi ) (5.9)

In the equation Eqn. (5.3) The cross-correlation vector term (rs) is inferred fromcentre column of auto-correlation matrix Rss. To concentrate on interference sig-nal alone the cross-correlation vector needs to be selected as center column ofauto-correlation matrix (Rnn). The optimum weight vector Wopt can be calcu-lated, as we have required entities. For this example considered above order ofoptimum weight vector Wopt is N*64 x 1. Following steps can be executed toobtain the wiener beamformer response s’(n):

1. Pick first 64 elements of Wopt , flip upside down and name it as wN .

2. Pick the next 64 elements and flip them upside down and name it as wN−1.

3. Continue this procedure till the end of Wopt i.e. till w1 is obtained.

Using these individual weight vectors filter N microphone responses in the array.Finally summing up all these outputs can retrieve desired output and process isdescribed in the figure shown below.

Figure 5.3: Wiener beamformer structure

19

Chapter 6

Evaluation metrics

These metrics are used to find quality of speech produced when a speech enhance-ment system is used. A speech communication system is expected to reproducesound to the listener, the same way it is received by the speech communicationsystem. This system is also expected to suppress interference or noise in the envi-ronment and enhance the desired sound. There are different metrics that help usevaluate speech communication system and in this thesis I used following metrics:

• Signal to noise ratio:

• Speech distortion normalized:

• Noise distortion normalized:

• Perceptual evaluation of speech quality:

6.1 Signal to Noise ratio

The signal to noise ratio can said as ratio between meaningful/ desired signal tobackground noise. In this experiment since there is no background noise intro-duced but the interference is considered as noise.

SNR =PsPn. (6.1)

(where Ps is average power of signal and Pn is average power of noise)Since most of the signals have varied dynamic range, logarithmic notation is usedto express SNRs decibel scale [4].

SNRdb = 10 log10

PsPn

(6.2)

Hence, signal to noise ratio improvement (SNRI) is given by,SNRI = SNROUT - SNRIN .

20

Evaluation metrics

6.2 Speech distortion normalized

The deviation of power spectral density of clear speech signal at an assumedreference microphone to the output signal received after processing speech is nor-malized speech distortion. A power level reference is obtained from enhancedoutput signal by normalizing target speech signal. The speech distortion is de-noted as:

SDnormalized = 10 log10

abs(Z1S(Ω)− S(Ω))

abs(S(Ω))(6.3)

Where Z1S(Ω) is,

Z1S(Ω) = NS × ZS(Ω) (6.4)

Here Ns is normalization constant given as,

NS =mean(S(Ω))

mean(ZS(Ω))(6.5)

Where S(Ω) and ZS(Ω) are power spectral density of S(n), Z(n) respectively.

6.3 Noise distortion normalized

The deviation of power spectral density of clear interference signal at an assumedreference microphone to the output signal received after processing speech istermed as normalized noise distortion. A power level reference is obtained fromoutput signal by normalizing target noise signal. The speech distortion is denotedas,

SDnormalized = 10 log10

abs(Z1I (Ω)− I(Ω))

abs(I(Ω))(6.6)

Where Z1s (Ω) is,

Z1I (Ω) = NI × ZI(Ω) (6.7)

Here NI is normalization constant given as,

NI =mean(I(Ω))

mean(ZI(Ω))(6.8)

Where I(Ω) and ZI(Ω) are power spectral density of I(n), Z(n) respectively.

6.4 Perceptual evaluation of speech quality (PESQ)

This is a family of standards with a test strategy to automatically assess thespeech quality as experienced by a user of hands free device, which is standard-ized by ITU-T recommendation P.862. The PESQ has a sensory model that

21

Evaluation metrics

compares the original signal to the degraded signal obtained after processing.This comparison results in a Mean Option Score (MOS), which is designed aftera sequence of tests conducted (MOS shown in Table 6.1) [17].

The PESQ takes into consideration various errors, coding distortions, packetloss, delay and variable delay, and filtering in analogue network components. Theinterface is designed such a way that recorded speech files or analogue connectionscan be easily accessed. Which can be used in many fields of business, educationand on any channels.

Table 6.1: Quality option score used in PESQ

Quality of speech score

Excellent 5Good 4Fair 3Poor 2Bad 1

6.5 Evaluation Procedure

The evaluation routine that we followed through out the thesis consists of thefollowing two steps:

• First, the beamformer is allowed to converge and all the associated weightsin the structure of the beamformer are saved.

• Now, the desired speech and the interference speech signals are filteredsolely using the above saved weights, in order to calculate the mentionedperformance metrics.

22

Chapter 7

Results

This chapter deals with the evaluation of the implemented beamformers. A stan-dardised environment is considered for all the beamforming algorithms in orderto compare their individual performances. We used a six element linear differ-ential microphone array throughout the thesis work. The intra-element distancebetween the microphones is 2.1375 cms. A female speaker recording and a malespeaker recording were used as input signals to the microphone array. The femalespeech is the target signal impinging from the forward path of the array and thedirection of arrival (DOA) was set at 45o to the centre of the array. The malespeech is the interference signal impinging from the backward path of the arrayand its DOA was set at −135o to the centre of the array. These two input signalswere sampled at 16 kHz frequency. Fractional delays have been considered. Thebeamforming algorithms were executed at different input SNR levels and theirperformances are compared using the metrics described in chapter 6. A vividdescription of the simulation set-up is as shown in Fig. 7.1.

Figure 7.1: Simulation set-up in anechoic chamber

23

Results

7.1 The Elko Beamformer

The Elko beamformer simulation set-up is as shown in the Fig. 7.2.

Figure 7.2: Elko Beamformer Simulation Set-up.

Normalized Least Mean Square (NLMS) Algorithm has been used to incurthe weight parameter while performing Elko beamforming.

The Elko algorithm has the ability to nullify one particular background noise/in-terference at a time giving signal-to-noise ratio increment (SNRI) of about 10 dB.It clearly enhances the distorted signal as shown in Fig 7.3.

Figure 7.3: Signal plots representing degraded and enhanced Signals of Elko Beam-former.

24

Results

The table below gives the SNRI, speech and noise distortion values underdifferent input SNR levels.

Table 7.1: Table showing results of elko beamformer under different input SNR levels

Input SNR SNRI Speech Distortion Noise Distortion0 dB 10.7451 -36.9375 -32.69085 dB 10.7778 -37.0730 -32.735710 dB 10.8007 -37.1781 -32.775715 dB 10.8129 -37.2341 -32.797320 dB 10.8179 -37.2572 -32.8063

Below figures are the observations while performing Elko Beamforming.

Figure 7.4: Graph representing input and output SNRs of Elko Beamformer

25

Results

Figure 7.5: Graph representing input and output PESQ MOS values of Elko Beam-former

Figure 7.6: Graph representing the Speech Distortion curves of Elko Beamformer at0dB input SNR

26

Results

Figure 7.7: Graph representing noise distortion curves of Elko Beamformer at 0dBinput SNR

From the above results, we can state that the performance of the Elko Beam-former is good with an average SNRI of about 10 dB and increment in the PESQMOS values are also observed. The suppression of the backward interference isthe significant observation while implementing Elko beamformer.

27

Results

7.2 The Wiener Beamformer

The simulation set-up of the wiener beamformer is as shown in Fig 7.8

Figure 7.8: The wiener beamformer simulation set-up

The wiener beamformer provides promising results and clearly enhances theinput distorted signal as indicated in the below Fig. 7.9

Figure 7.9: Signal plots representing degraded and enhanced Signals of Wiener Beam-former

28

Results


Table 7.2: Table showing results of wiener beamformer under different input SNRlevels


Below figures are the observations while performing Wiener Beamforming.

Figure 7.10: Graph representing input and output SNRs of wiener beamformer

29

Results

Figure 7.11: Graph representing speech distortion curves of the wiener beamformerat 0dB input SNR

Figure 7.12: Graph representing noise distortion curves of wiener beamformer at 0dBinput SNR

30

Results

Figure 7.13: Graph representing input and output PESQ MOS values of wienerBeamformer

From the above results, we can state that the performance of the wienerbeamformer is quite promising with an average SNRI of about 15 dB and theincrement in the PESQ MOS values representing a good quality speech signal.The wiener beamformer is then used to get signals from the desired direction.

31

Results

7.3 Elko Wiener Beamformer

Figure 7.14: Elko Wiener Beamformer simulation setup

Elko-Wiener Beamformer has the advantages of both the Elko and WienerBeamformer. SNRI values of Elko-Wiener Beamformer is significantly appreciablethan the Elko and Weiner Beamformers individual SNRI values. Also PESQ MOSvalues of Elko-Wiener Beamformer values represent a high quality speech signal.Elko-Wiener Beamformer clearly enhances the distorted signal as shown in thefigure.

Figure 7.15: Elko-Wiener Signal Plots representing degraded and enhanced signals

32

Results


Table 7.3: Table showing results of elko-wiener beamformer under different inputSNR levels


Below figures are the observations while performing Elko-Wiener Beamform-ing.

Figure 7.16: Graph representing input and output SNRs of Elko-Wiener Beamformer

33

Results

Figure 7.17: Graph representing the Speech Distortion curves of Elko-Wiener Beam-former at 0dB input SNR

Figure 7.18: Graph representing the Noise Distortion curves of Elko-Wiener Beam-former at 0dB input SNR

34

Results

Figure 7.19: Graph representing input and output PESQ MOS values of Elko-WienerBeamformer

From the above results, we can state that the Elko-Wiener Beamformer pro-vides quite promising results and sustains a high level of performance with anaverage SNRI of about 28dB. The rise in the PESQ MOS values representing ahigh quality speech signal. Hence, The Elko-Wiener beamformer could be used forapplications in which the user wants to nullify the backward signals and preferredto get signals from the desired direction.

35

Results

7.4 Results Comparison Plots

In the following plots all the three implemented beamformers results are com-pared.

Figure 7.20: Input vs. output SNRs comparison plot

36

Results

Figure 7.21: SNR 3D comparison plot

Figure 7.22: PESQ MOS comparison plot

37

Chapter 8

Summary and Conclusion

Enhancement of noisy speech signals has been investigated with three differentbeamforming techniques applied to linear array of differential microphones. Theseare: a well-known Elko beamforming technique, Wiener beamformer and a newElko-Wiener beamformer. One of the major contributions of this thesis is theaccomplished description of these techniques given in chapter 5 and 4. The Elkobeamformer minimizes the first order differential microphone output power bylocating its null in the rear-half plane. The Wiener beamformer minimizes themean square difference between the beamformer output and a single microphoneoutput. WBF is typically implemented using omnidirectional microphones. How-ever, in the present thesis work it has been developed using back-to-back cardioiddirectional microphones. The assemblage of these two beamformers has beenimplemented as the Elko-Wiener beamformer, which is now proved to be advan-tageous than the individual beamformers. Concerning the simulation of thesebeamformers, two speech recordings sampled at 16 KHz have been used as in-puts to the six element linear array of differential microphones. The DOA’s ofthe target and interference signals was set at 45o and −135o to the centre ofthe array. These algorithms were implemented in a computer simulated anechoicchamber and a similar test environment has been considered in order to comparetheir performances. The Elko algorithm was successful in cancelling backwardinterference from any position. The Wiener beamformer was quite promising inextracting the target signal from the desired direction. Hence, the Elko-Wienerbeamformer has the ability to nullify the backward signals and preferred to getthe signals from the desired direction.

Taking these constraints into account, the performance of the linear differen-tial microphone array using the above mentioned beamformers has been simulatedand evaluated by means of three measures, SNRI, distortion measures and theITU-T recommended PESQ MOS values. The Elko algorithm has a SNR im-provement of 10dB and posses a rapid adaptation with less computational com-plexity. The Wiener beamformer has a SNR improvement of 17dB with moderatecomplexity. The Elko-Wiener beamformer outperforms the individual Elko andWiener beamformer and possesses a SNR improvement of 28dB. The cost of thishigh level performance is its computational complexity. The simulation time is

38

Summary and Conclusion

very high compared to the individual two beamformers.Finally, it must be pointed out that the Wiener beamformer plays active role

in getting the desired results. If the target and interference both are in the samedirection of the plane then the Wiener beamformer itself will enhance the noisyspeech with minimal target speech distortion.

39

Chapter 9

Future Work

The fact that the proposed beamformer concedes promising results could en-courage us to work for the improvement of such beamformer. The followingsuggestions for future work are given:

• During the course of work, only interference has been considered in therear half plane of the array. The Elko algorithm significantly removes theinterference from any position in the rear half plane of the linear array.However, if multiple interferers are used in the rear half plane then just byusing series of Elko algorithms would be very convenient to remove all theunwanted sources in rear half plane. It would be very useful and challengingto find such results.

• Although the Elko-Wiener beamformer presents better performance thanthe individual Elko and Wiener beamformers, the computational complexityand simulation time are still rather coarse and hence it is suggested to takenecessary steps in avoiding such circumstances.

• While linear microphone arrays delivering proficient results, beamformersshould imply different approach by using other array geometries like circulararrays, semi circular arrays, etc.

40

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