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Cellular Communications. 5. Speech Coding. Low Bit-rate Voice Coding. Voice is an analogue signal Needed to be transformed in a digital form (bits) Speech signal is not random=>can be encoded using fewer bits as compared to random signal - PowerPoint PPT Presentation
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CELLULAR COMMUNICATIONS5. Speech Coding
Low Bit-rate Voice Coding Voice is an analogue signal Needed to be transformed in a digital
form (bits) Speech signal is not random=>can be
encoded using fewer bits as compared to random signal
If bits representing 1sec of speech can transferred over wireless channel during 200ms=> can pack 5 signals into the channel
For a handset transmitting less bits is alsoe means longer battery life
Requirement for speech coding Can distort a speech a little bit (lossy)
but should preserve acceptable quality
Shouldn’t be to complex Use less power Use less circuits Reduce delay
Hierarchy of speech coders
Waveform Coders vs. VOCODERS Waveform coders
Approximate any acoustic signal
VOCODERS Based on prior knowledge of the signal Speech signals are very special signals
Speech signals Not all levels of a speech signal are
equally likely High probabilities of very low amplitudes Significant probability of very high
amplitudes Monotonically decreasing probabilities of
amplitudes between these two extremes Speech is predictable
The next value of a speech signals can be predicted with large probability and fair precision from the past samples
Speech in frequency domain Power of high frequency components is
small High frequency components when
present are very important for speech quality
Sampling and quantization Speech signal is analog, measured at
infinitely many time instances and infinitely many possible values
Sampling: measure signal at finite time instances (sampling interval)
Quantization: approximate infinitely many possible values by finite number of possible values (e.g. 8 bits)
Uniform quantization Divide the range of all possible values
into finite number of equal intervals Assign single quantization value to all
values within the interval
Non-uniform quantization Divide the range of all possible values into
finite number of unequal but equally probable intervals
Logarithmic quantization: smaller intervals at low amplitudes
Different weight to low values US: -Law Europe: A-low
-Law
A-law
Adaptive quantization Adjust to input signal power
Rate-Distortion Theorem Shannon: There existing a mapping from source
waveform to code words such that for given distortion (error) D, R(D) bits per sample is sufficient to restore signal with an average distortion arbitrary close to D
R(D) is called rate distortion formula (achievable low bound)
Scalar quantization does not achieve this bound
Vector quantization Encode a segment of
sampled analog signal (e.g. L samples)
Use codebooks of n vectors Segment all possible
samples of dimension L into areas of equal probability
Very efficient at very low rates( R=0.5 bits per sample)
Learning codebook
LBG: Split areas (double codebook)
Adaptive Differential Pulse Code Modulation
PCM Each sample representing by its amplitude
(8 bits) Standard telephony: 8K samples per
second, 8 bit per sample= 64kbps DPCM
Encode only difference from previous sample
Smaller differences are more often Use less bits to represent smaller
differences(4 bits) but more bits (10 bits) to represent larger differences
DPCM and prediction
ADPCM Use more complex prediction in a
transmitter/receiver to estimate next sample value
Transmitter send only difference between estimation and real value
Lossy codec: transmit approximate differences
Hopefully difference will be small
Frequency Domain Coding of Speech
Divide speech signal into a set of frequency components
Quantize and encode each component separately
Control number of bits/quality allocate to each band
Sub-band coding Human ear does not detect error at all
frequencies equally well
SBC
Vocoders Model speech signal generation process Transmitter analyze the voice signal
according to assumed model Transmitter sends parameters driveled
from the analysis Receiver synthesize voice based on
received parameters Vocoders are much more complex that
waveform coders but achieve higher economy in a bit rate
Human Vocal Tract
Voice Generation Model
LPC
Advanced codecs CELP
Transmitter/Receiver share common pitch codebook
Search for most suitable pitch code
RELP Transmit model parameters Also transmit Residual(differences) signal
Mean Opinion Score Quality Rating
Codec MOS rating