Letters

MPEG-4 ALS—International Standardfor Lossless Audio Coding
Takehiro Moriya†, Noboru Harada, Yutaka Kamamoto,and Hiroshi Sekigawa
Abstract
This article explains the technologies and applications of lossless audio coding. NTT started researchon lossless coding of audio signals and proposed the initiation of international standardization in 2002,
aiming at high-quality services suitable for broadband networks. As a result of cooperative work withother organizations, the specifications of this technology were officially incorporated in the ISO/IEC
MPEG standard published in March 2006 (ISO: International Standards Organization, IEC: International Electrotechnical Commission, MPEG: moving picture experts group).

1. BackgroundIn parallel with the evolution of broadband networks and digital audio equipment, information rates
for delivery and storage keep growing rapidly inresponse to demands for high-quality audio signals(high sampling rates, high word resolution, and multiple channels). NTT Communication Science Laboratories recognized the importance of lossless compression of audio signals and the standardization ofits technology, considering interoperability, longterm maintenance, and clear status of intellectual
property rights. The Laboratories took the initiativein promoting this technology and advancing it to a
standard in MPEG (moving picture experts group),
which is a working group within ISO/IEC (International Standards Organization, International Electrotechnical Commission).
NTT initiated discussion on the need for andrequirements for such a standard and prepared thetechnical call for technologies. Through the normal
standardization process, several improvements andintegration efforts were applied to the initial reference model. The partners for this standardization
included the Technical University of Berlin (Ger† NTT Communication Science Laboratories
Atsugi-shi, 243-0198 Japan
E-mail: moriya@idea.brl.ntt.co.jp40many), RealNetworks Corp. (USA), and I2R (Singapore).
As a result of the final ballot in December 2005, thespecifications of the lossless coding were officiallyestablished as “14496-4 3rd edition amendment 2(ALS: audio lossless coding)” and published inMarch 2006 [1]-[5]. Note that, in parallel, MPEG-4
also established MPEG-4 SLS (scalable lossless coding), which provides the special functionality oflossy-to-lossless compression and bit-rate scalablecompression. In this article, we focus on the moregeneral compression tool: MPEG-4 ALS.
As shown in Fig. 1, MPEG audio standards havemade significant contributions to communication
systems including broadcasting, mobile services, andInternet services. Most of the audio coding standards,
such as MP3 and AAC or the technology for theMiniDisc system*1, are based on perceptual codingwith a high compression ratio in exchange for minor
waveform distortion at the decoder. These encoders
carefully control the quantization distortion by utilizing the characteristics of the human ear. The waveform is different from the original, although it is perceptually very close to it.
Unlike perceptual coding, lossless coding ensures
perfect reconstruction of the waveform without a sin*1 MiniDisc: System introduced by Sony Corporation as a digital
replacement for conventional analog compact audio cassettes.

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Letters

Lossless

Perceptual coding (lossy)
Mobile digital
broadcasting

SLS

SBR
SSC

MP3

Satellite/terrestrial
digital broadcasting

MPEG-2
MC/LSF

MPEG-2AAC

ALS

MP3 on 4

MPEG-4V1 V2 2001

2005

DST

Extension of MPEG-4

MPEG-1
1992

1994

1996

1998

2000

2002

MC/LSF: multi-channel and low sampling frequency
AAC: advanced audio coder
SBR: spectral band replication

2004

2006

SSC: sinusoidal coding
SLS: scalable lossless
DST: direct stream transfer

Fig. 1. History of MPEG audio.
Table 1. Comparison of perceptual coding and lossless coding.
Perceptual coding

Lossless coding

Compression ratio

5–10%

15–70%

Difference from original

Perceptually no difference

Strictly no difference

Applications

Broadcasting, portable players

Storage, editing, other signals
besides audio

Methods, standards

MPEG (MP3, AAC), MiniDisc

Some free software, SACD,
DVD-audio

SACD: super audio compact discgle bit of difference. This property is very important
for applications such as waveform editing and thearchiving of high-quality audio signals. The cost of
perfect reconstruction is a limited compression ratio
and hence larger compressed file sizes, which range
from 15 to 70% of the original file size depending on
the statistical properties of the original waveform.
These comparisons are summarized in Table 1.
2. Technical description
2.1 Fundamental principles
This technology is based on time-domain linear
prediction. The fundamental processing of encoding
and decoding is shown in Fig. 2. NTT Laboratories is
one of the pioneer contributors to the development oflinear prediction technology, which has been widelyused as an essential tool in speech coding systems
such as cellular phones and IP (Internet protocol)
phones. Linear prediction analysis estimates prediction parameters that minimize the errors caused by
prediction from a certain number of past samples.
Integer values of the prediction error signal are transVol. 4 No. 8 Aug. 2006

mitted to the decoder. The decoder can reconstruct
the original waveform without a single bit error fromthe prediction parameters and the prediction error
signal. The prediction error signal actually has small
amplitude values and they can be compressed bymeans of an entropy coding method such as Ricecode, as shown in Table 2. It is obvious that thesmaller the amplitude, the shorter the code length. Inparallel, prediction parameters are actually converted
to PARCOR*2 coefficients and quantized. The coefficients are also compressed with a similar Rice code.
PARCOR coefficients are convenient for quantization and stability checking. The prediction order canbe adaptively set from 0 (no prediction) to 1023.
Moreover, progressive order prediction is used at theinitial samples of the starting frames of the random*2 PARCOR (partial auto correlation): A set of predictive parameters
invented by the Musashino Electrical Communication Laboratories of Nippon Telegraph and Telephone Public Corporation (nowNTT) in 1972. This set has the property of stability and easy quantization, so it is widely used for speech coding and synthesis andin other signal processing areas.

41Letters

Integer
input–

Entropycoding
Prediction error(magnified by 30 times)

Short-term
prediction
Analysis andquantization

×30

Inversequantization

Input waveform
Short-term
prediction

Integer
output+Entropy
decodingFig. 2. Fundamental structure of encoder and decoder.
Table 2. Example of Rice code.
Binary code

Amplitude
4111111110

31111110

211110

1110

00

–1

10–21110–3111110–411111110

access points.
2.2 Long-term prediction and multi-channel
prediction
Linear prediction utilizes the correlation between
neighboring samples. Speech and audio signals
sometimes have long-term correlation due to thepitch. In addition, there is inter-channel correlation
between multiple channels. All these correlations canbe utilized to enhance the prediction performance andhence reduce the bit rate. The extended prediction
scheme is shown in Fig. 3 [6], [7]. In the encoder, 5tap (5 independent coefficients) long-term prediction
and 3- or 6-tap multi-channel prediction are sequentially applied to the short-term prediction error signal
to reduce the amplitude.
For long-term prediction, the best delay parameterfrom the previous sample and the associated weighting factor are determined. For multi-channel coding,channel-pair combinations are searched for to get themaximum inter-channel correlation. In addition, therelative delay parameter between the channel-pair42

and the associated weighting factors are determined.
All these weighting factors are quantized and compressed by Rice code.
2.3 Floating-point signals
The extended scheme for floating-point signals,
which makes use of the fundamental compression
structure for the integer signal, is shown in Fig. 4 [8][10]. The floating-point format is useful for professional mixing of music because there is no risk ofoverflow or underflow. However, this format cannot
be compressed because its nominal value has no correlation between samples. In response, we invented anovel scheme that decomposes a floating-point signal
sequence into an integer sequence and the remaining
sequence. We also invented the ACF (approximatecommon factor) scheme, which has achieved a great
improvement in the compression performance when
the input floating-point value sequence is generated
from an integer value sequence multiplied by a common number throughout the frame. The common
number can be detected by means of rational approximations even though the input samples have errors
due to truncation or other operations. The remaining
sequence is further compressed by masked LZ*3 compression, making full use of the properties inheritedfrom the decomposition processes.
2.4 Other features
The ALS standard has some additional operation*3 LZ (Lempel-Ziv): Universal lossless compression tool invented
by Lempel and Ziv and used in ZIP. This tool adaptively updates
the codebook depending on the input sequence and is useful for thecompression of text and program source code.

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Letters

Multichannel
input––

Short-term Long-term
prediction prediction

Interchannel
prediction

EntropycodingInterchannel
prediction

Entropy
decoding

Analysis andquantization

Inversequantization
Short-term Long-term
prediction prediction++

Multichannel
outputFig. 3. Extensions for long-term and multi-channel prediction.

Floating-point
input
Commonfactor division––

EntropycodingShort-term
prediction

Floating
conversion
Analysis andquantization

Masked LZ coding

Masked LZ decoding+

Floating
conversion

Inversequantization

Floating-point
outputShort-term
prediction+

Entropy
decoding

Fig. 4. Extension for floating-point input.

modes. One is a backward prediction mode, which
attains efficient compression performance at the costof longer processing times in the encoder anddecoder. Another mode uses a hierarchical adaptive
block switching mechanism for higher compression
performance. Furthermore, there are several othercontrol parameters at the encoder, which may be efficiently estimated in the future without losing conformity to the standard.
3. Practical evaluation
ALS is compared with other available lossless coding tools (free software and MPEG-4 SLS) in terms
Vol. 4 No. 8 Aug. 2006

of compression ratio (the smaller the better) anddecoding time (the smaller the better) in Fig. 5. In
addition, the performance of NTT’s proprietary optimized decoder is shown. We can see from this figurethat the standard provides the state-of-the-art performance. Note that the compression performance ofALS outperforms that of general-purpose compression tools such as ZIP, so far as audio signals are concerned.
This standard accepts a wide range of waveform
formats as input for compression, as shown in Table
3, and can be used for most of the audio-related applications. Encoding can be executable in real time
while the music is being played back on a low-end
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Letters(%)
MPEG-4 ALS(reference decoder)

Compression ratio

50

MPEG-4 SLS(scalable)

49
Freeware A
48

Freeware B

4746
MPEG-4 ALS(NTT proprietary decoder)450510(seconds)
Average decoding times for 30 seconds of various music (48,96,192 kHz)

Fig. 5. Comparison of compression performances.
Table 3. Waveform formats supported by MPEG-4 ALS.
MPEG-4 ALS

CD (reference)Max. sampling frequency

192 kHz44.1 kHz

Max. amplitude resolution

32 bits

16 bits

65,5362Integer and floating-point value

IntegerMax. number of channels
Formats

ContentsServer

ClientUploadinglosslessly
compressed fileTransmission
over network

Reduced download
time for CD
over B-FLET’S(15 min → 7 min)
Reducing storage
cost

Simultaneous
downloading anddecoding

Fig. 6. Example of application scenario (music delivery).

personal computer, and decoding can be executed
more than ten times faster than the playback time. In
other words, the load on the central processing unit
for decoding is negligible for most applications. Fora typical server/client music delivery system (Fig. 6),
the total download time can be significantly reduced
because the download file size is significantly
reduced, decoding time is less than the download
time, and decoding can be executed in parallel with44

downloading. This holds true even if an optical fiber
high-speed connection is used.
4. Future tasks
It is expected that this standard will be used forcommon tools for various applications and will continue to be maintained so that compressed files can beperfectly decoded even 100 years in the future. It isNTT Technical Review

Letters

also expected that a consortium of essential patent
holders will be organized for collecting and delivering the patent royalties.
NTT Communication Science Laboratories willcontinue to support the standardization of conformance and reference software and the enhancementof encoder performance. In parallel, NTT Communications will design and provide integrated delivery
and archiving systems making use of practical software that complies with this standard. In addition,
NTT Group companies will collaborate with partners
or issue licenses to other organizations for various
applications including professional audio editing
tools, portable music players, and editing and archiving systems for medical and environmental data.
References[1]“International Standardization of Lossless Coding Technology for
Audio Signals,” NTT Technical Review, Vol. 4, No. 4, p. 55, 2006.
[2] ISO/IEC 14496-3:2005/Amd.2:2006, Information technology—Coding of audio-visual objects—Part 3: Audio, Amendment 2: Audio
Lossless Coding (ALS), new audio profiles and BSAC extensions,edition 2006-03-15.
[3] T. Moriya, D. Yang, and T. Liebchen, “Extended Linear Prediction
Tools for Lossless Audio Coding,” Proceedings of ICASSP 2004, pp.
III-1008-1011, 2004.
[4] T. Liebchen, Y. Reznik, T. Moriya, and D. Yang, “MPEG-4 Audio
Lossless Coding,” Preprint paper #6047, 116th Audio Engineering
Society Convention, Berlin, 2004.
[5] T. Liebchen, T. Moriya, N. Harada, Y. Kamamoto, and Y. Reznik,“The MPEG-4 Audio Lossless Coding (ALS) Standard Technologyand Applications,” Preprint paper #6589, 119th Audio Engineering
Society Convention, New York, 2005.
[6] Y. Kamamoto, T. Moriya, T. Nishimoto, and S. Sagayama, “Losslesscompression of multi-channel signals based on inter-channel correlation,” IPSJ, Vol. 46, No. 5, pp. 1118 - 1128, 2005 (in Japanese).
[7] Y. Kamamoto, T. Moriya, N. Harada, T. Nishimoto, and S. Sagayama, “Intra- and Inter-Channel Long-Term Prediction in ISO/IEC
MPEG-4 Audio Lossless Coding (ALS),” IEICE Trans. on Communications, Vol. J89-B, No. 2, pp. 214-222, 2006 (in Japanese).
[8] D. Yang and T. Moriya, “Lossless Compression for Audio Sources
with IEEE Floating Point Format,” Preprint 115th Audio Engineering
Society Convention, #5987, 2003.
[9] N. Harada, T. Moriya, H. Sekigawa, and K. Shirayanagi, “LosslessCompression of IEEE Floating-point Audio using the ApproximateCommon-Factor Coding,” Preprint paper #6352, 118th Audio Engineering Society Convention, Barcelona, 2005.
[10] N. Harada, T. Moriya, H. Sekigawa, K. Shirayanagi, and Y. Kamamoto, “Lossless Compression of IEEE754 Floating-Point Signal in
ISO/IEC MPEG-4 Audio Lossless Coding (ALS),” IEICE Trans. onCommunications, Vol. J89-B, No. 2, pp. 204-213, 2006 (in Japanese).

Vol. 4 No. 8 Aug. 2006

Takehiro Moriya
Executive Manager, NTT R&D Fellow,
Human and Information Science Laboratory,
NTT Communication Science Laboratories.
He received the B.S., M.S., and Ph.D. degrees
in applied mathematics and instrumentation
physics from the University of Tokyo, Tokyo, in
1978, 1980, and 1989, respectively. After joining
the Musashino Electrical Communication Laboratories of Nippon Telegraph and TelephonePublic Corporation (now NTT) in 1980, heengaged in research on and the standardization ofspeech and audio coding. In 1989, he stayed atAT&T Bell Laboratories as a guest researcher.
He is a member of the Acoustical Society ofJapan (ASJ), the Information Processing Societyof Japan (IPSJ), and the Institute of Electronics,
Information and Communication Engineers(IEICE) of Japan and a fellow of IEEE.

Noboru Harada
Research Scientist, Human and Information
Science Laboratory, NTT Communication Science Laboratories.
He received the B.S. and M.S. degrees in computer science and systems engineering fromKyushu Institute of Technology, Fukuoka, in1995 and 1997, respectively. He joined NTT
Human Interface Laboratories, Tokyo, in 1997.
His main research area has been lossless audio
coding and high-efficiency coding of speech andaudio. He is a member of ASJ, IEICE, and IEEE.

Yutaka KamamotoResearcher, Human and Information Science
Laboratory, NTT Communication Science Laboratories.
He received the B.S. degree in applied physics
from Keio University, Kanagawa, in 2003 andthe M.S. degree in information physics and computing from the University of Tokyo, Tokyo, in2005. Since joining NTT Communication Science Laboratories in 2005, he has been studying
signal processing and information theory. He is amember of ASJ, IPSJ, the Society of Information
Theory and its Applications, and IEICE.

Hiroshi Sekigawa
Senior Research Scientist, Human and Information Science Laboratory, NTT Communication Science Laboratories.
He received the B.S. and M.S. degrees in mathematics and the Ph.D degree in mathematical sciences from the University of Tokyo, Tokyo, in
1986, 1989, and 2004, respectively. He joined
NTT in 1989. He was engaged in research on
computer-aided design for LSIs from 1989 to1994 and on symbolic and algebraic computationand symbolic-numeric computation from 1994 to2000. In 2000, he was temporarily transferred toBusiness Communications Headquarters, NTTWest, Osaka. Since his return to NTT Communication Science Laboratories in 2002, he has been
engaged in research on symbolic-numeric computation and its application to science and engineering problems. He is a member of IPSJ, theJapan Society for Symbolic and Algebraic Computation, IEICE, and the Japan Society for Industrial and Applied Mathematics.

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