1. Govt. Polytechnic Dhangar(Fatehabad)
Multimedia Application

2. Audio Compression Techniques
Hello,
Today I will talk about the common techniques commonly used for digital audio compression of various audio filetype formats.

3. Introduction Digital Audio Compression Applications
Removal of redundant or otherwise irrelevant information from audio signal
Audio compression algorithms are often referred to as “audio encoders”
Applications
Reduces required storage space
Reduces required transmission bandwidth
-I will discuss the difference between redundant and irrelevant further in my presentation.
-Depending on storage or transmission, there is an optimization in size

4. Audio Compression Audio signal – overview
Sampling rate (# of samples per second)
Bit rate (# of bits per second). Typically, uncompressed stereo 16-bit 44.1KHz signal has a 1.4MBps bit rate
Number of channels (mono / stereo / multichannel)
Reduction by lowering those values or by data compression / encoding

5. Audio Data Compression
Redundant information
Implicit in the remaining information
Ex. oversampled audio signal
Irrelevant information
Perceptually insignificant
Cannot be recovered from remaining information

6. Audio Data Compression
Lossless Audio Compression
Removes redundant data
Resulting signal is same as original – perfect reconstruction
Lossy Audio Encoding
Removes irrelevant data
Resulting signal is similar to original

7. Audio Data Compression
Audio vs. Speech Compression Techniques
Speech Compression uses a human vocal tract model to compress signals
Audio Compression does not use this technique due to larger variety of possible signal variations

8. Generic Audio Encoder Psychoacoustic Model
Psychoacoustics – study of how sounds are perceived by humans
Uses perceptual coding
eliminate information from audio signal that is inaudible to the ear
Detects conditions under which different audio signal components mask each other

9. Psychoacoustic Model Signal Masking
Threshold cut-off
Spectral (Frequency / Simultaneous) Masking
Temporal Masking
Threshold cut-off and spectral masking occur in frequency domain, temporal masking occurs in time domain

10. Signal Masking Threshold cut-off Spectral Masking
Hearing threshold level – a function of frequency
Any frequency components below the threshold will not be perceived by human ear
Spectral Masking
A frequency component can be partly or fully masked by another component that is close to it in frequency
This shifts the hearing threshold

11. Signal Masking Temporal Masking
A quieter sound can be masked by a louder sound if they are temporally close
Sounds that occur both (shortly) before and after volume increase can be masked

12. Spectral Analysis Tasks of Spectral Analysis
To derive masking thresholds to determine which signal components can be eliminated
To generate a representation of the signal to which masking thresholds can be applied
Spectral Analysis is done through transforms or filter banks

13. Spectral Analysis Transforms Fast Fourier Transform (FFT)
Discrete Cosine Transform (DCT) - similar to FFT but uses cosine values only
Modified Discrete Cosine Transform (MDCT) [used by MPEG-1 Layer-III, MPEG-2 AAC, Dolby AC-3] – overlapped and windowed version of DCT

14. Spectral Analysis Filter Banks
Time sample blocks are passed through a set of bandpass filters
Masking thresholds are applied to resulting frequency subband signals
Poly-phase and wavelet banks are most popular filter structures

15. Filter Bank Structures
Polyphase Filter Bank [used in all of the MPEG-1 encoders]
Signal is separated into subbands, the widths of which are equal over the entire frequency range
The resulting subband signals are downsampled to create shorter signals (which are later reconstructed during decoding process)

16. Filter Bank Structures
Wavelet Filter Bank [used by Enhanced Perceptual Audio Coder (EPAC) by Lucent]
Unlike polyphase filter, the widths of the subbands are not evenly spaced (narrower for higher frequencies)
This allows for better time resolution (ex. short attacks), but at expense of frequency resolution

17. Noise Allocation
System Task: derive and apply shifted hearing threshold to the input signal
Anything below the threshold doesn’t need to be transmitted
Any noise below the threshold is irrelevant
Frequency component quantization
Tradeoff between space and noise
Encoder saves on space by using just enough bits for each frequency component to keep noise under the threshold - this is known as noise allocation

18. Noise Allocation Pre-echo
In case a single audio block contains silence followed by a loud attack, pre-echo error occurs - there will be audible noise in the silent part of the block after decoding
This is avoided by pre-monitoring audio data at encoding stage and separating audio into shorter blocks in potential pre-echo case
This does not completely eliminate pre-echo, but can make it short enough to be masked by the attack (temporal masking)

19. Additional Encoding Techniques
Other encoding techniques techniques are available (alternative or in combination)
Predictive Coding
Coupling / Delta Encoding
Huffman Encoding

20. Additional Encoding Techniques
Predictive Coding
Often used in speech and image compression
Estimates the expected value for each sample based on previous sample values
Transmits/stores the difference between the expected and received value
Generates an estimate for the next sample and then adjusts it by the difference stored for the current sample
Used for additional compression in MPEG2 AAC

21. Additional Encoding Techniques
Coupling / Delta encoding
Used in cases where audio signal consists of two or more channels (stereo or surround sound)
Similarities between channels are used for compression
A sum and difference between two channels are derived; difference is usually some value close to zero and therefore requires less space to encode
This is a case of lossless encoding process

22. Additional Encoding Techniques
Huffman Coding
Information-theory-based technique
An element of a signal that often reoccurs in the signal is represented by a simpler symbol, and its value is stored in a look-up table
Implemented using a look-up tables in encoder and in decoder
Provides substantial lossless compression, but requires high computational power and therefore is not very popular
Used by MPEG1 and MPEG2 AAC

23. Encoding - Final Stages
Audio data packed into frames
Frames stored or transmitted