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This article explores the principles and techniques of low bit-rate speech coding crucial for effective cellular communications. It discusses the transformation of speech signals into digital formats and the importance of efficient coding to preserve voice quality while minimizing battery consumption and complexity. Key coding methods, including waveform and vocoders, are examined alongside concepts such as sampling, quantization, and rate-distortion theory. Advanced codecs such as CELP and RELP are highlighted for their effectiveness in voice transmission.
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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 • 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 also 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
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
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
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
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
Advanced codecs • CELP • Transmitter/Receiver share common pitch codebook • Search for most suitable pitch code • RELP • Transmit model parameters • Also transmit Residual(differences) signal
