p. 1 DSP-II DSP Everywhere… Applications of DSP in Audio and Digital Communications Simon Doclo...

p. 1DSP-II

DSP Everywhere…Applications of DSP in Audio and

Digital Communications

Simon Doclo

Dept. Elec. Engineering, K.U.Leuven

[email protected]

www.esat.kuleuven.ac.be/~doclo/

14/12/01 Simon Doclo DSP Everywhere… p. 2

• DSP in Digital Communications

• 3G-systems: UMTS, CDMA

• Wireless systems: GSM, WLAN

• Modems: Cable, ADSL, VDSL

• Line echo cancellation

Introduction

• Satellite communications

• Optical communication


• DSP in Audio Applications

Introduction

• Hearing aids / cochlear implants

• Hands-free telephony

• Tele-conferencing

• Voice-controlled systems

• Audio effects

• Audio and speech coding


• Other applications

• Medical applications

• Cryptography

• Process control in chemical, pharmaceutical, energy plants

• Image and video processing

• …

Introduction

Anywhere (digital) signals are present, DSP-techniques are required!


Overview

• Introduction

• DSP in digital communications systems:– xDSL-modems: modulation, equalisation

• DSP in audio applications:– Hands-free communication: echo, noise and reverberation– Basic techniques:

• Acoustic echo cancellation (AEC)

• Multi-microphone beamforming

– Application: hearing aids

• Conclusion


Telephone Line modems

• High-speed data communication:– optical, cable, wireless, telephone line

• Telephone Line Modems– voice-band modems : up to 56kbits/sec in 0…4kHz band– ADSL modems : up to 6Mbits/sec in 30kHz…1MHz band– VDSL modems : up to 52Mbits/sec in …10MHz band

• Time to download 10 Mbyte-file:

Modem Time

56 Kbps voice-band modem 24 minutes

128 Kbps ISDN 10 minutes

6 Mbps ADSL 13 seconds

52 Mbps VDSL 1.5 seconds


xDSL Modems

• ADSL : ‘Asymmetric Digital Subscriber Line’• HDSL : ‘High Speed Digital Subscriber Line’• VDSL : ‘Very High Speed Digital Subscriber Line’ …-1993: ADSL spurred by interest in video-on-demand (VOD)1995 : ADSL/VOD interest decline1996 : ADSL technology trials prove viability.1997-... : ADSL deployment, reoriented to data applications, as telco’s reaction to cable operators offering high- speed internet access with cable modems2000-… : VDSL


xDSL Modems

• Analog/digital telephone network: BW 3 kHz, SNR 35 dB Shannon capacity

• ADSL/VDSL: higher bandwidth, lower SNR + impairments• Bitrate depends on length of copper line

Upstream

Downstream

SubscriberCentral office

Copper wire

vb.

300 m6.4 Mbps52 MbpsVDSL

3 km640 Kbps6 MbpsADSL

LengthUpDown

12 MHz

1.1 MHz

Bandwidth

kbits/sec35)1(log. 2 SNRBW


• Modulation-technique : DMT (Discrete Multitone)• Basic idea:

– Decompose frequency into ‘tones’ (FFT/IFFT)– Assign bits according to SNR per tone

– ADSL spec (=ANSI standard): • 256 tones, 512-point (I)FFTs• carrier spacing fo= 4.3215 kHz, basic sampling rate 2.21 MHz

(=512*4.3215 kHz)

– VDSL (=proposal): up to 4096 tones, same carrier spacing

DMT Principles: IFFT/FFT-based modulation

frequentie

SNR

frequentie

bits/toon


ADSL Spectrum

• ADSL spectrum :


• Frequency-dependent channel attenuation introduces inter-symbol interference (ISI) equalization

• Coupling between wires in same or adjacent binders introduces crosstalk (XT)– Near-end Xtalk (NEXT)– Far-end Xtalk (FEXT)– Other systems (e.g. HPNA)

• Radio Frequency Interference (RFI): e.g. AM broadcast, amateur radio

• Noise: e.g. impulsive noise (=high bursts of short duration)• Echo: due to hybrid impedance mismatch echo cancellation

Conclusion: Need advanced modulation, DSP,etc. !

Communication impairments (1)

useful signal

FEXTNEXT


Communication impairments (2)

• ADSL channel attenuation, crosstalk, noise


Modulation - Demodulation (1)

• DMT-transmission block scheme:

S/P

FFT

IFFT P/S

Discreteequivalent

channel

4-QAM

8-QAM

Bitstream

(freq domain)

Modulation

(IFFT)

Time domain

signal

Demodulation

(FFT)

Equalisation

FEQ


Modulation - Demodulation (2)

• Transmission: modulation is realized by means of 2N-point Inverse Discrete Fourier Transform (IFFT) (example N=4 )

• Receiver: demodulation with inverse operation, i.e. FFT

domainfrequency in symbolth -m

*1

*2

*3

4

3

2

1

0

matrixDFT inverse

49423528211470

42363024181260

35302520151050

2824201612840

211815129630

14121086420

76543210

00000000

domainin time symbolth m

7

6

5

4

3

2

1

0

.

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

a

a

a

a

a

a

a

a

WWWWWWWW

WWWWWWWW

WWWWWWWW

WWWWWWWW

WWWWWWWW

WWWWWWWW

WWWWWWWW

WWWWWWWW

s

s

s

s

s

s

s

s

Nj

eW

.

realreal


The Magic Prefix Trick (1)

Additional feature : before transmission, a ‘prefix’

is added to each time-domain

symbol, i.e. the last

samples are copied and

put up front :

domain-in time symbolth -m

7

6

5

4

3

2

1

0

in -addprefix

88

22

sequence dtransmitte

7

6

5

4

3

2

1

0

8

7

.0

m

m

m

m

m

m

m

m

x

x

m

m

m

m

m

m

m

m

m

m

s

s

s

s

s

s

s

s

I

I

s

s

s

s

s

s

s

s

s

s

)2 (example



Prefix insertion : • in the receiver, the samples corresponding to the

prefix are removed (=unused) :

S/P

FFT

FEQ

IFFT P/S

Discreteequivalent

channel



• if channel impulse response has length L (= L non-zero taps) and ( is prefix length), then all ‘transient effects’ between symbols are confined to the prefix period :

1L

Tx-side Rx-side

Tone 3

Tone 2

Tone 1

Tone 0

Tone 3

Tone 2

Tone 1

Tone 0

Prefix From IFFT Guardband To FFT

*

ch(t)

r(t)s(t)

Channel



• Magic trick fails if , resulting in

– inter-symbol-interference (ISI) = interference from previous symbol(s) (same carrier)

– inter-carrier interference (ICI) = interference from other carriers

• In the receiver, after removing the samples corresponding to the prefix, the i-th tone is observed, multiplied by a factor H(i.fo), i.e. the channel response for frequency f=i.fo

• ‘Prefix trick’ is based on a linear convolution (filtering by channel impulse response) being turned into a circular convolution, which corresponds to component-wise multiplication in frequency domain easy equalization !

1L


Overview

• Introduction






• Conclusion


Hands-free communication

• Recorded microphone signals are corrupted by:• Far-end echoes acoustic echo cancellation• Acoustic background noise noise suppression• Room reverberation dereverberation

• Application: hands-free telephony, hearing aids, voice control


Signal model: some maths…

• Multi-microphone signal enhancement algorithms:– Extract clean speech/audio signal from microphone recordings– Exploit spatial and frequency diversity between speech and noise

• Microphone signals (m=1…M):

• Output signal:

compute filters g[k] :– echo cancellation:– noise reduction/dereverberation:

• gm[k] cancels noise components• gm[k] focuses on speech s[k]

echo endfarnoiseionreverberat

][][][][][][

kxkhknkskhky fmmmm

][][][][][1

kxkgkykgkz fM

mmm

][kyM

][2 ky

][1 ky ][1 kg

][2 kg

][kg M

][kz

][kx

][kg f

+

+

+

_

unknown

][][ khkg fm

f

known (=loud-speaker signal)


Overview

• Introduction






• Conclusion


Acoustic echo cancellation (AEC)

Suppress acoustic and line echo:– to guarantee normal conversation conditions : users do not like to

hear a delayed and filtered version of their own voice– to prevent the closed-loop system from becoming unstable if

amplification is too high


• Propagation of sound waves in an acoustic environment results in– signal attenuation– spectral distortion

• The attenuation and distortion can be modeled quite well as a linear filtering operation

• Non-linear distortion mainly stems from the loudspeakers. Its effect is typically of second order, therefore (often) not taken into account

• The linear filter h[k] modeling the acoustic path between loudspeaker and microphone is represented by the acoustic impulse response

Room Acoustics


– direct path impulse and early reflections, which depend on the geometry of the room

Acoustic Impulse Response (1)

– dead time

Different parts:

– an exponentially decaying tail called reverberation, coming from multiple reflections

– For typical applications the impulse reponse is between 100 and 400 ms long several 100 to 1000 taps @ 8-16 kHz memory requirement for circular buffers in DSP

– Because people move around in the recording room, the acoustic impulse response is highly time-varying


Acoustic Impulse Response (2)

ESAT speech laboratory :

T60 120 ms

Paleis voor Schone Kunsten :

T60 1500 ms

Original speech signal :

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (sec)

Am

plitu

de

Impulse response PSK row 9

0 0.02 0.04 0.06 0.08 0.1 0.12

-0.2

-0.1

0

0.1

0.2

0.3

Time (sec)

Am

plitu

de

Impulse response SpeechLab


Acoustic Impulse Response : FIR or IIR ?

• If the acoustic impulse response is modeled as– an FIR filter many hundreds to several thousands of

filter taps are required– an IIR filter filter order can be reduced, but still several

hundreds of filter coefficients are required (=‘bad’ model for acoustic impulse response)

– Remark: IIR-filters good model for classical filters (LP,HP,BP,BS)

• hence FIR models are typically used in practice – as they are guaranteed to be stable– as adaptive filtering techniques are called for:

• FIR adaptive filters are easier than IIR adaptive filters


AEC based on Adaptive Filtering

• Goal: Identify acoustic impulse response h[k] and subtract filtered loudspeaker signal from microphone signal

• Thanks to the adaptivity – time-varying acoustics can be tracked– AEC is ‘self-learning’– performance superior to performance of conventional techniques

][kyM

][2 ky

][1 ky ][1 kg

][2 kg

][kg M

][kz

][kx

][kg f

+

+

+

_


Adaptive Filtering Algorithms

• Algorithm: 2 steps– Filter loudspeaker signal error signal indicates how close

this signal is to recorded microphone signal– Update filter: update depends on error signal

error signaldesired signal

filtered signal


Normalized Least Mean Square (NLMS)

with

L is the adaptive filter length, is the adaptation stepsize, is a regularization parameter and k is the discrete-time index

]1[

][

Lkx

kx

k x

][

][][][

][

1 ke

kykdke

ky

kk

Tk

kk

kTk

xxx

ww

wx

Data filtering

Filter update

]1[

]0[

Lw

w

k wCircular data buffer

Filter coefficients


Control Algorithm

• ‘AEC is more than just an adaptive filter’ :

– adaptive filter is supplemented with control software, which mainly controls the adaptation speed (e.g. no adaptation during double-talk)

– In practice echo suppression is limited to 30 dB due to time-variance, non-linearities, finite filterlength postprocessing (e.g. center-clipping)


Real-time DSP Implementation (1)

• AEC-implementation on DSP (lab equipment):– TMS320C44 @ 50 MHz : data acquisition (ADC/DAC) – TMS320C40 @ 50 MHz : acoustic echo cancellation

(AEC)

AEC ADC/DAC



• Adaptive filtering part : several algorithms can be selected– NLMS : time-domain algorithm– PB-FDAF : frequency-domain algorithm (better performance)

• Control software– double-talk detection– non-linear postprocessing algorithm

• Variable sampling rate – Common sampling rates for speech applications: 8 kHz, 16 kHz– for audio applications: 22.05 kHz, 44.1 kHz, 48 kHz

• Echo paths up to 325 ms can be modeled and tracked with the FDAF based on LMS at 8 kHz sampling frequency and 16 ms delay



Execution times for the most important blocks of the DSP code were measured :

N=768

FFT-size=128

fs=8000 Hz

block 64 samples = 8 ms


Demo

Output AEC

Near-end signal

Output AEC

Double-talk without

Detection

Local

speaker

Far-end signal


Overview

• Introduction






• Conclusion


Beamforming basics

• Background/history: antenna array design for RADAR• Array elements are combined electronically such that:

– array can be steered towards specific direction higher directivity– beam shaping is possible

• Beamforming for hands-free communication : – focus beam on speech source(s) speech enhancement and

dereverberation– put spatial nulls in direction of noise sources noise reduction

• Classification:– fixed beamforming: data-independent fixed filters gm[k]

e.g. delay-and-sum, weighted-sum, filter-and-sum

– adaptive beamforming: data-dependent adaptive filters gm[k]e.g. LCMV-beamformer, Generalized Sidelobe Canceller


• Microphone signals are delayed and summed together array can be virtually steered to angle

• Angular selectivity is obtained, based on constructive ( =) and destructive ( ) interference

• Uniform delay-and-sum beamforming implies– Uniform array equal inter-microphone distance – Uniformly distributed delays

Delay-and-sum beamforming (1)

d

cos)1( dm

d

2

m

1

M

1

M

eG

mj

m

)(

M

mmm ky

Mkz

1

][1

][

sm

m fc

d cos

dmdm )1(

)1(mm


• Spatial directivity pattern H(,) for uniform DS-beamformer

• H(,) has sinc-like shape and is frequency-dependent


)2/sin(

)2/sin(

),(

2/

2/1

)cos(cos)1(

j

jM

M

m

fc

dmj

e

Me

eHs

02000

40006000

8000 045

90135

180

0.2

0.4

0.6

0.8

1

Angle (deg)Frequency (Hz)

-20

-10

0

90

270

180 0

Spatial directivity pattern for f=5000 Hz

M=5 microphonesd=3 cm inter-microphone distance=60 steering anglefs=16 kHz sampling frequency


• For an ambiguity, called spatial aliasing, occurs.

This is analogous to time-domain aliasing where now the spatial sampling (=d) is too large.


cos1

d

cf

sf

cd max

0

2000

4000

6000

8000 050

100150

0.20.40.60.8

1

Angle (deg)

Frequency (Hz)

M=5, =60, fs=16 kHz, d=8 cm

Spatial aliasing


• Better directivity patterns than DS-beamformer are obtained with weighted-sum and filter-and-sum beamformers– e.g. Frequency-independent directivity pattern

Filter-and-sum beamformer

][kyM

][2 ky

][1 ky ][1 kg

][2 kg

][kg M

][kz

][kx

][kg f

+

+

+

_

M=8Logarithmic arrayL=50=90 fs=8 kHz

01000

20003000

045

90135

180

0

0.5

1

Angle (deg)

Frequency (Hz)


• Adaptive filter-and-sum structure:– Minimize noise output power, while maintaining a chosen frequency

response in look direction (and/or other linear constraints)– LCMV = Linearly Constrained Minimum Variance

• minimize variance of output z[k]

• in order to avoid desired signal to be distorted or cancelled out,J linear constraints are added

Adaptive beamforming

][kyM

][2ky

][1ky

][1ky ][1kg ][1kf ][2kg

][1kf ][kgM

][1kf

][kz

][1ky

Speaker

Noise

gRggg

][min][min 2 kkzE yyT

JJMLT with bCbgC ,,


Generalized Sidelobe Canceller (1)

• GSC consists of three parts:– Fixed (delay-and-sum) beamformer, in order to achieve spatial

alignment of speech source speech reference– Blocking matrix, placing spatial nulls in the direction of the speech

source noise references– Multi-channel adaptive filter with delay

Postproc


• Blocking matrix Ca :– creating maximum M-1 independent noise references by placing

spatial nulls in look-direction– different possibilities: e.g. Griffiths-Jim, Walsh

broadside

Generalized Sidelobe Canceller (2)

1111TC

1001

0101

0011TaC

1111

1111

1111TaC 0

20004000

60008000 0 45 90 135 180

0

0.5

1

1.5

Angle (deg)

Frequency (Hz)

• Problems of GSC:– impossible to reduce noise from look-direction– reverberation effects cause signal leakage in noise reference adaptive filter is only updated when no speech is present !


Overview

• Introduction






• Conclusion


Application: Hearing Aids (1)

• Hearing problems are very common nowadays• Most of the users are dissatisfied with the performance of

their hearing aid in noisy environments (cocktail party effect)

increase speech intelligibility by reducing background noise• Traditional hearing aids:

– one microphone, analog, limited signal processing– amplification of all incoming sound without distinction between

different sound sources

• Enabling technologies:– microphone miniaturisation integrate multiple microphones into

one hearing aid– micro-electronics: size ASIC < 10 mm2, low power consumption– advanced DSP techniques (noise reduction, feedback suppression)


• Improvement of speech intelligibility by reduction of background noise• BTE hearing aid with 2 (or more) closely-spaced microphones• GSC in switched mode:

• Beamfomer : weights can be adapted during speech• Noise suppression (ANC) : only adaptation during noise• Speech detection : determine when speech is present

Application: Hearing Aids (2)

Noise Source

Result

Noise reference

Delay 1

Adaptive filter 1

Speech reference

Adaptive filter 2

Delay 2

Speech detection

Speech source Beamformer Noise suppression


Conclusion

• DSP-techniques can be found in many every-day products:– audio applications: CD, MiniDisc, hands-free telephony– communications: GSM, modems, WLAN– medical applications: hearing aids, cochlear implants

• Implementation differences:– sampling rate, memory requirements, complexity

• Basic techniques:– filters, filterbanks, FFT/IFFT frequency filtering– adaptive filters track changing systems– multi-sensor systems spatial filtering

p. 1 DSP-II DSP Everywhere… Applications of DSP in Audio and Digital Communications Simon Doclo...

Documents

Transcript of p. 1 DSP-II DSP Everywhere… Applications of DSP in Audio and Digital Communications Simon Doclo...