Speech & Audio Coding TSBK01 Image Coding and Data Compression - - PowerPoint PPT Presentation

Speech & Audio Coding

TSBK01 Image Coding and Data Compression Lecture 11, 2003 Jörgen Ahlberg

Outline

• Part I - Speech

– Speech – History of speech synthesis & coding – Speech coding methods

• Part II – Audio

– Psychoacoustic models – MPEG-4 Audio

Speech Production

• The human’s vocal

apparatus consists of:

– lungs – trachea (wind pipe) – larynx

• contains 2 folds of skin

called vocal cords which blow apart and flap together as air is forced through

–

• ral tract

– nasal tract

• The Speech Signal

The Speech Signal

• The Speech Signal

• The Speech Signal

• History of Speech Coding

• History of Speech Coding

• µ

• Y 1
• Source-filter Model of Speech Production

Speech Coding Strategies

• 1. PCM
• Invented 1926, deployed 1962.
• The speech signal is sampled at 8 kHz.
• Uniform quantization requires >10 bits/sample.
• Non-uniform quantization (G.711, 1972)
• Quantizing y to 8 bits -> 64 kbit/s.

Speech Coding Strategies

• 2. Adaptive DPCM
• Example: G.726 (1974)
• Adaptive predictor based on six previous

differences.

• Gain-adaptive quantizer with 15 levels 32

kbit/s.

Speech Coding Strategies

• 3. Model-based Speech Coding
• Advanced speech coders are based on

models of how speech is produced:

Excitation source Vocal tract

An Excitation Source Noise generator Pulse generator Pitch

Vocal Tract Filter 1: A Fixed Filter Bank BP

g1

BP

g2

BP

gn

Vocal Tract Filter 2: A Controllable Filter

Linear Predictive Coding (LPC)

• The controllable filter is modelled as

yn = ∑ ai yn-i + Gεn where εn is the input signal and yn is the output.

• We need to estimate the vocal tract parameters (ai

and G) and the exciatation parameters (pitch, v/uv).

• Typically the source signal is divided in short

segments and the parameters are estimated for each segment.

• Example: The speech signal is sampled at 8 kHz and

divided in segments of 180 samples (22.5 ms/segment).

Typical Scheme of an LPC Coder

Noise generator Pulse generator Pitch Vocal tract filter v/uv Gain Filter coeffs

Estimating the Parameters

• v/uv estimation

– Based on energy and frequency spectrum.

• Pitch-period estimation

– Look for periodicity, either via the a.c.f our some

• ther measure, for example

that gives you a minimum value when p equals the pitch period. – Typical pitch-periods: 20 - 160 samples.

Estimating the Parameters

• Vocal tract filter estimation

– Find the filter coefficients that minimize the error ε2 = ( yn - ∑ ai yn-i + Gεn )2 – Compare to the computation of optimal predictors (Lecture 7).

Estimating the Parameters

• Assuming a stationary signal:

where R and p contain acf values.

• This is called the autocorrelation method.

Estimating the Parameters

• Alternatively, in case of a non-stationary

signal: where

• This is called the autocovariance method.

Example

• Coding of parameters using LPC10 (1984):

54 bits 2.4 kbit/s Sum: 1 bit Synchronization 46 bits Unvoiced filter 46 bits Voiced filter 6 bits Pitch 1 bit v/uv

The Vocal Tract Filter

• Different representations:

– LPC parameters – PARCOR (Partial Correlation Coefficients) – LSF (Line Spectrum Frequencies)

• Code Excited Linear Prediction Coding (CELP)

Examples

• G.728

– V(z) is chosen as a large FIR-filter (M 50). – The gain and FIR-parametrers are estimated recursively from previously received samples. – The code book contains 127 sequences.

• GSM

– The code book contains regular pulse trains with variabel frequency and amplitudes.

• MELP

– Mixed excitation linear prediction – The code book is combined with a noise generator.

Other Variations

• SELP – Self Excited Linear Prediction
• MPLP – Multi-Pulse Excited Linear Prediction
• MBE – Multi-Band Excitation Coding

Quality Levels

Bitrate Bandwidth Quality level <4 kbit/s Synthetic quality 4 – 16 kbit/s Communication quality 16 – 64 kbit/s 300 – 3400 kHz Network (tool) quality >64 kbit/s 10 kHz Broadcast quality

• Subjective Assessment

• Subjective Assessment