Govt. Polytechnic Dhangar(Fatehabad) Multimedia Application

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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)

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

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

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

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

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