This document provides an overview of MPEG-1 audio compression. It describes the key components of the MPEG-1 audio encoder including the polyphase filter bank that transforms audio into frequency subbands, the psychoacoustic model that determines inaudible parts of the signal, and the coding and bit allocation process that assigns bits to subbands. The overview concludes by noting that MPEG-1 audio provides high compression while retaining quality and paved the way for future audio compression standards.

1. A Tutorial on
MPEG/Audio
Compression
Davis Pan, IEEE Multimedia Journal,
Summer 1995
Presented by:
Randeep Singh Gakhal
CMPT 820, Spring 2004

2. Outline
 Introduction
 Technical Overview
 Polyphase Filter Bank
 Psychoacoustic Model
 Coding and Bit Allocation
 Conclusions and Future Work

3. Introduction
 What does MPEG-1 Audio provide?
A transparently lossy audio compression system based on
the weaknesses of the human ear.
 Can provide compression by a factor of 6 and
retain sound quality.
 One part of a three part standard that includes
audio, video, and audio/video synchronization.

4. Technical Overview

5. MPEG-I Audio Features
 PCM sampling rate of 32, 44.1, or 48 kHz
 Four channel modes:
 Monophonic and Dual-monophonic
 Stereo and Joint-stereo
 Three modes (layers in MPEG-I speak):
 Layer I: Computationally cheapest, bit rates > 128kbps
 Layer II: Bit rate ~ 128 kbps, used in VCD
 Layer III: Most complicated encoding/decoding, bit rates ~
64kbps, originally intended for streaming audio

6. Human Audio System (ear + brain)
 Human sensitivity to sound is non-linear
across audible range (20Hz – 20kHz)
 Audible range broken into regions where
humans cannot perceive a difference
 called the critical bands

7. MPEG-I Encoder Architecture[1]

8. MPEG-I Encoder Architecture
 Polyphase Filter Bank: Transforms PCM samples
to frequency domain signals in 32 subbands
 Psychoacoustic Model: Calculates acoustically
irrelevant parts of signal
 Bit Allocator: Allots bits to subbands according to
input from psychoacoustic calculation.
 Frame Creation: Generates an MPEG-I compliant
bit stream.

9. The Polyphase
Filter Bank

10. Polyphase Filter Bank
 Divides audio signal into 32 equal width
subband streams in the frequency domain.
 Inverse filter at decoder cannot recover
signal without some, albeit inaudible, loss.
 Based on work by Rothweiler[2].
 Standard specifies 512 coefficient analysis
window, C[n]

11. Polyphase Filter Bank
 Buffer of 512 PCM samples with 32 new
samples, X[n], shifted in every computation cycle
 Calculate window samples for i=0…511:
 Partial calculation for i=0…63:
 Calculate 32 subsamples:
][][][ iXiCiZ ⋅=
∑=
+=
7
0
]64[][
j
jiZiY
∑=
⋅=
63
0
]][[][][
k
kiMiYiS

12. Polyphase Filter Bank
 Visualization of the filter[1]
:

13. Polyphase Filter Bank
 The net effect:
 Analysis matrix:
 Requires 512 + 32x64 = 2560 multiplies.
 Each subband has bandwidth π/32T centered at
odd multiples of π/64T
]64[]64[]][[][
63
0
7
0
jiXjiCkiMiS
k j
++= ∑ ∑= =





 −+
=
64
)16)(12(
cos]][[
πki
kiM

14. Polyphase Filter Bank
 Shortcomings:
 Equal width filters do not correspond with critical
band model of auditory system.
 Filter bank and its inverse are NOT lossless.
 Frequency overlap between subbands.

15. Polyphase Filter Bank
 Comparison of filter banks and critical bands[1]:

16. Polyphase Filter Bank
 Frequency response of one subband[1]
:

17. Psychoacoustic
Model

18. The Weakness of the Human Ear
 Frequency dependent resolution:
 We do not have the ability to discern minute
differences in frequency within the critical bands.
 Auditory masking:
 When two signals of very close frequency are
both present, the louder will mask the softer.
 A masked signal must be louder than some
threshold for it to be heard  gives us room to
introduce inaudible quantization noise.

19. MPEG-I Psychoacoustic Models
 MPEG-I standard defines two models:
 Psychoacoustic Model 1:
 Less computationally expensive
 Makes some serious compromises in what it
assumes a listener cannot hear
 Psychoacoustic Model 2:
 Provides more features suited for Layer III
coding, assuming of course, increased processor
bandwidth.

20. Psychoacoustic Model
 Convert samples to frequency domain
 Use a Hann weighting and then a DFT
 Simply gives an edge artifact (from finite window
size) free frequency domain representation.
 Model 1 uses 512 (Layer I) or 1024 (Layers II
and III) sample window.
 Model 2 uses a 1024 sample window and two
calculations per frame.

21. Psychoacoustic Model
 Need to separate sound into “tones” and “noise”
components
 Model 1:
 Local peaks are tones, lump remaining spectrum per
critical band into noise at a representative frequency.
 Model 2:
 Calculate “tonality” index to determine likelihood of each
spectral point being a tone
 based on previous two analysis windows

22. Psychoacoustic Model
 “Smear” each signal within its critical band
 Use either a masking (Model 1) or a spreading
function (Model 2).
 Adjust calculated threshold by incorporating
a “quiet” mask – masking threshold for
each frequency when no other frequencies
are present.

23. Psychoacoustic Model
 Calculate a masking threshold for each subband in the
polyphase filter bank
 Model 1:
 Selects minima of masking threshold values in range of each
subband
 Inaccurate at higher frequencies – recall how subbands are
linearly distributed, critical bands are NOT!
 Model 2:
 If subband wider than critical band:
 Use minimal masking threshold in subband
 If critical band wider than subband:
 Use average masking threshold in subband

24. Psychoacoustic Model
 The hard work is done – now, we just
calculate the signal-to-mask ratio (SMR)
per subband
 SMR = signal energy / masking threshold
 We pass our result on to the coding unit
which can now produce a compressed
bitstream

25. Psychoacoustic Model (example)
 Input[1]
:

26. Psychoacoustic Model (example)
 Transformation to perceptual domain[1]
:

27. Psychoacoustic Model (example)
 Calculation of masking thresholds[1]
:

28. Psychoacoustic Model (example)
 Signal-to-mask ratios[1]
:

29. Psychoacoustic Model (example)
 What we actually send[1]
:

30. Coding and Bit
Allocation

31. Layer Specific Coding
 Layer specific frame formats[1]
:

32. Layer Specific Coding
 Stream of samples is processed in groups[1]
:

33. Layer I Coding
 Group 12 samples from each subband and
encode them in each frame (=384 samples)
 Each group encoded with 0-15 bits/sample
 Each group has 6-bit scale factor

34. Layer II Coding
 Similar to Layer I except:
 Groups are now 3 of 12 samples per-subband =
1152 samples per frame
 Can have up to 3 scale factors per subband to
avoid audible distortion in special cases
 Called scale factor selection information (SCFSI)

35. Layer III Coding
 Further subdivides subbands using Modified
Discrete Cosine Transform (MDCT) – a lossless
transform
 Larger frequency resolution => smaller time
resolution
 possibility of pre-echo
 Layer III encoder can detect and reduce pre-echo
by “borrowing bits” from future encodings

36. Bit Allocation
 Determine number of bits to allot for each
subband given SMR from psychoacoustic model.
 Layers I and II:
 Calculate mask-to-noise ratio:
 MNR = SNR – SMR (in dB)
 SNR given by MPEG-I standard (as function of quantization
levels)
 Now iterate until no bits to allocate left:
 Allocate bits to subband with lowest MNR.
 Re-calculate MNR for subband allocated more bits.

37. Bit Allocation
 Layer III:
 Employs “noise allocation”
 Quantizes each spectral value and employs
Huffman coding
 If Huffman encoding results in noise in excess of
allowed distortion for a subband, encoder
increases resolution on that subband
 Whole process repeats until one of three
specified stop conditions is met.

38. Conclusions and
Future Work

39. Conclusions
 MPEG-I provides tremendous compression
for relatively cheap computation.
 Not suitable for archival or audiophile grade
music as very seasoned listeners can
discern distortion.
 Modifying or searching MPEG-I content
requires decompression and is not cheap!

40. Future Work
 MPEG-1 audio lays the foundation for all modern
audio compression techniques
 Lots of progress since then (1994!)
 MPEG-2 (1996) extends MPEG audio
compression to support 5.1 channel audio
 MPEG-4 (1998) attempts to code based on
perceived audio objects in the stream
 Finally, MPEG-7 (2001) operates at an even
higher level of abstraction, focusing on meta-data
coding to make content searchable and
retrievable

41. References
[1] D. Pan, “A Tutorial on MPEG/Audio Compression”,
IEEE Multimedia Journal, 1995.
[2] J. H. Rothweiler, “Polyphase Quadrature Filters – a New
Subband Coding Technique”, Proc of the Int. Conf. IEEE
ASSP, 27.2, pp1280-1283, Boston 1983.