1. Dept. Elec. Engineering, K.U.Leuven
DSP Everywhere… Applications of DSP in Audio and Digital Communications
Simon Doclo
Dept. Elec. Engineering, K.U.Leuven

2. Introduction DSP in Digital Communications Wireless systems: GSM, WLAN
3G-systems: UMTS, CDMA
Wireless systems: GSM, WLAN
Modems: Cable, ADSL, VDSL
Line echo cancellation
Satellite communications
Optical communication
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3. Introduction DSP in Audio Applications Audio and speech coding
Audio effects
Audio and speech coding
Tele-conferencing
Voice-controlled systems
Hands-free telephony
Hearing aids / cochlear implants
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4. Introduction
Other applications
Anywhere (digital) signals are present, DSP-techniques are required!
Medical applications
Cryptography
Process control in chemical, pharmaceutical, energy plants
Image and video processing …
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5. 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
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6. x 1000 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:
x 1000
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
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7. 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)
: ADSL/VOD interest decline
: ADSL technology trials prove viability.
: ADSL deployment, reoriented to data applications,
as telco’s reaction to cable operators offering high-
speed internet access with cable modems
2000-… : VDSL
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8. 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
Subscriber
Central office
Copper wire
300 m
6.4 Mbps
52 Mbps
VDSL
3 km
640 Kbps
6 Mbps
ADSL
Length Up
Down
12 MHz
1.1 MHz
Bandwidth
vb.
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9. DMT Principles: IFFT/FFT-based modulation
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= kHz, basic sampling rate 2.21 MHz (=512* kHz)
VDSL (=proposal): up to 4096 tones, same carrier spacing
frequentie
SNR
bits/toon
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10. ADSL Spectrum
ADSL spectrum :
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11. Communication impairments (1)
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. !
useful signal
FEXT
NEXT
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12. Communication impairments (2)
ADSL channel attenuation, crosstalk, noise
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13. Modulation - Demodulation (1)
DMT-transmission block scheme:
S/P
FFT
IFFT
P/S
Discrete
equivalent
channel
4-QAM
8-QAM
Bitstream
(freq domain)
FEQ
Modulation
(IFFT)
Time domain
signal
Demodulation
(FFT)
Equalisation
Simon Doclo DSP Everywhere…

14. 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
real
real
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15. 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 :
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16. The Magic Prefix Trick (2)
Prefix insertion :
in the receiver, the samples corresponding to the prefix are removed (=unused) :
IFFT
P/S
S/P
Discrete
equivalent
channel
FFT
FEQ
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17. The Magic Prefix Trick (3)
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 :
Tx-side
Rx-side
Channel
Tone 3
Tone 2
Tone 1
Tone 0
Tone 3
Tone 2
Tone 1
Tone 0
ch(t) *
s(t)
r(t)
Prefix
From IFFT
Guardband
To FFT
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18. The Magic Prefix Trick (4)
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 !
Simon Doclo DSP Everywhere…

19. 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
Simon Doclo DSP Everywhere…

20. 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
Simon Doclo DSP Everywhere…

21. 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]
unknown
known (=loud-speaker signal)
Simon Doclo DSP Everywhere…

22. 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
Simon Doclo DSP Everywhere…

23. 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
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24. Room Acoustics
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
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25. Acoustic Impulse Response (1)
Different parts:
dead time
direct path impulse and early reflections, which depend on the geometry of the room
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 kHz memory requirement for circular buffers in DSP
Because people move around in the recording room, the acoustic impulse response is highly time-varying
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26. Acoustic Impulse Response (2)
ESAT speech laboratory :
T60  120 ms
Paleis voor Schone Kunsten :
T60  1500 ms
Original speech signal :
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27. 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
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28. 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
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29. 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
filtered signal
desired signal
error signal
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30. 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
Data filtering
Filter update
Circular data buffer
Filter coefficients
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31. 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)
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32. Real-time DSP Implementation (1)
AEC-implementation on DSP (lab equipment):
50 MHz : data acquisition (ADC/DAC)
50 MHz : acoustic echo cancellation (AEC)
AEC
ADC/DAC
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33. Real-time DSP Implementation (2)
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: 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
Simon Doclo DSP Everywhere…

34. Real-time DSP Implementation (3)
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
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35. Demo Local speaker Output AEC Output AEC Far-end signal without
Double-talk
without
Detection
Far-end signal
Near-end signal
Simon Doclo DSP Everywhere…

36. 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
Simon Doclo DSP Everywhere…

37. 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
Simon Doclo DSP Everywhere…

38. Delay-and-sum beamforming (1)
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
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39. Delay-and-sum beamforming (2)
Spatial directivity pattern H(,) for uniform DS-beamformer
H(,) has sinc-like shape and is frequency-dependent
M=5 microphones
d=3 cm inter-microphone distance
=60 steering angle
fs=16 kHz sampling frequency
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40. Delay-and-sum beamforming (3)
For an ambiguity, called spatial aliasing, occurs. This is analogous to time-domain aliasing where now the spatial sampling (=d) is too large.
M=5, =60, fs=16 kHz, d=8 cm
Spatial aliasing
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41. Filter-and-sum beamformer
Better directivity patterns than DS-beamformer are obtained with weighted-sum and filter-and-sum beamformers
e.g. Frequency-independent directivity pattern
M=8
Logarithmic array
L=50
=90
fs=8 kHz
Simon Doclo DSP Everywhere…

42. Adaptive beamforming 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
Simon Doclo DSP Everywhere…

43. 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
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44. Generalized Sidelobe Canceller (2)
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
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 !
Simon Doclo DSP Everywhere…

45. 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
Simon Doclo DSP Everywhere…

46. 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)
Simon Doclo DSP Everywhere…

47. Application: Hearing Aids (2)
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
Simon Doclo DSP Everywhere…

48. 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
Simon Doclo DSP Everywhere…