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CELLULAR COMMUNICATIONS 5. Speech Coding

Cellular Communications

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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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Page 1: Cellular Communications

CELLULAR COMMUNICATIONS5. Speech Coding

Page 2: Cellular Communications

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

Page 3: Cellular Communications

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

Page 4: Cellular Communications

Hierarchy of speech coders

Page 5: Cellular Communications

Waveform Coders vs. VOCODERS Waveform coders

Approximate any acoustic signal

VOCODERS Based on prior knowledge of the signal Speech signals are very special signals

Page 6: Cellular Communications

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

Page 7: Cellular Communications

Speech in frequency domain Power of high frequency components is

small High frequency components when

present are very important for speech quality

Page 8: Cellular Communications

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)

Page 9: Cellular Communications

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

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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

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-Law

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A-law

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Adaptive quantization Adjust to input signal power

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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

Page 15: Cellular Communications

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)

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Learning codebook

LBG: Split areas (double codebook)

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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

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DPCM and prediction

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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

Page 20: Cellular Communications

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

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Sub-band coding Human ear does not detect error at all

frequencies equally well

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SBC

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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

Page 24: Cellular Communications

Human Vocal Tract

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Voice Generation Model

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LPC

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Advanced codecs CELP

Transmitter/Receiver share common pitch codebook

Search for most suitable pitch code

RELP Transmit model parameters Also transmit Residual(differences) signal

Page 28: Cellular Communications

Mean Opinion Score Quality Rating

Page 29: Cellular Communications

Codec MOS rating