Lossless audio coder

Details Lossless audio coder Lossless audio coder

2001-01-05

2004-01-06

McFadden, Susan (Department: 2655)

Data processing: speech signal processing, linguistics, language

Audio signal bandwidth compression or expansion

C704S503000

Reexamination Certificate

active

06675148

ABSTRACT:

TECHNICAL FIELD
This invention relates generally to the coding of audio signals and more particularly to a method of lossless compression of audio data for use in the transmission and/or storage of audio information.
BACKGROUND
Over the past ten to twenty years, the audio industry has seen a major transition from analog formats, such as cassette tapes, FM radio, and records to new digital formats such as the compact disc (CD), mini-disks (MD), digital versatile disks (DVD), and others. The widespread use of personal computers and the Internet has furthered this trend with the introduction of new electronic music services that allow electronic distribution of music and/or other audio content through a computer and the Internet. Many of these digital audio products and services use various audio compression technologies (e.g., MP3, Dolby AC3, ATRACS, MPEG-AAC, and Windows Media Player) to reduce the bit rate of audio transmissions to the range of 64-256 kbps from the 1440 kbps used on many uncompressed recordings, such as CDs, while maintaining a sufficient quality of high fidelity music reproduction. The use of compression technologies as well as the increased storage capacity of semiconductor (i.e., SRAM, DRAM, and Flash) devices and computer disks has made possible several new products including the RIO portable music player, the AudioRequest music jukebox, the Lansonic™ Digital Audio Server, and other devices.
In a typical digital audio application, an analog audio signal is sampled, for example, at 32, 44.1, or 48 kHz, and then is digitized with 16 or more bits using an analog-to-digital converter. If the audio source is a stereo source, then this process may be repeated for both the right and left channels. New surround sound audio may have six or more channels, each of which may be sampled and digitized. A typical CD contains two stereo channels, each of which is sampled at 44.1 kHz with 16 bits per sample, resulting in a data rate of approximately 1411.2 kbps. This allows storage of slightly more than 1 hour of music on a 650 MB CD. In a playback application, the digital music samples may be converted to an analog signal using a digital-to-analog converter, and then amplified and played through one or more speakers.
Several audio compression techniques may be used to compress a stereo music signal to the range of 64-256 kbps without significantly changing the quality of the audio signal (i.e., while maintaining CD-like quality). The MPEG-1 standard, developed and maintained by a working group of the International Standards Organization (ISO/IEC), describes three audio compression methods, referred to as Layers 1, 2, and 3, for reducing the bit rate of a digital audio signal. The method described under Layer 3, which is commonly known as MP3, is generally considered to achieve acceptable quality at 128 kbps and very good quality at 256 kbps.
These audio compression methods, as well as some other lossy techniques, use frequency domain coding techniques with a complex psychoacoustic model to discard portions of the audio signal that are considered inaudible. The techniques may be used to achieve near-CD quality at compression ratios of about, for example, 5-to-1 (256 kbps) or 11-to-1 (128 kbps). However, psychoacoustic modeling is an inexact process and some approaches may introduce artifacts into the audio signal that may be audible and annoying to some listeners. As a result, lossy compression may be less desirable in some applications requiring very high audio quality.
In the absence of any compression, the storage capacity of current consumer hard drives is quite limited. A large capacity hard drive, such as one with a capacity of 60-80 GB, can only store approximately 95-125 hours of uncompressed CD-quality music. In contrast, a CD changer may hold as many as 400 discs, providing over 400 hours of audio. As a result, some method of significantly increasing the amount of audio that can be stored on a hard drive without increasing cost or adding artifacts is useful.
One method of increasing the amount of data that can be stored is to compress the data before storing the data and then to expand the compressed data when needed. In lossy compression methods such as MP3, the expanded data differs slightly from the original data. For audio and video signals, this may be acceptable as long the differences are not too significant. However, for computer data, any difference may be unacceptable. As a result, lossless compression methods for which the expanded data are identical to the original uncompressed data have been developed. Various lossless or “entropy” coders attempt to remove redundancies from data (for example, after every “q” there is a “u”) and exploit the unequal probability of certain types of data (for example, vowels occur more often than other letters). Computer programs such as “tar” and “ZIP” have been developed to perform lossless compression on documents and other computer files. These algorithms are typically based on methods developed by Ziv and Lempel or use other standard method such as Huffman coding or Arithmetic coding techniques (see, for example, T. Bell et. al., “Text Compression”, Prentice-Hall, 1990).
Unfortunately, many lossless coding techniques designed for text or other computer-type data do not perform well on digital audio data. In fact, programs such as “ZIP” actually may enlarge an audio file rather than compressing the file. The problem is that these techniques assume certain features that may be common in text files but are not typically found in audio data.
Methods for lossless compression of audio typically attempt to compress an audio file by exploiting certain redundancies in the audio signal. Generally, these redundancies can be applied either in the time domain via prediction or in the frequency domain via bit allocation. In addition, entropy coding can be applied to take advantage of the varying probability of different data values by assigning shorter sequences of bits to represent higher probability values and longer sequences of bits to represent lower probability values. The result is a reduction in the average number of bits required to represent all of the data values. These advantages have resulted in the incorporation of lossless compression into the DVD-Audio format (see, “Meridian Lossless Packing Enabling High-Resolution Surround on DVD-Aduio”, MIX, December 1998).
One technique for lossless compression is to divide the audio signal into segments or frames. Then, for each frame, to compute a low-order linear predictor that is quantized and stored for that frame. This predictor then may be applied to all the audio samples in the frame, and the prediction residuals (i.e., the error after prediction) may be coded using some form of entropy-type coder, such as, for example, a Huffman, Golomb, Rice, run-length, or arithmetic coder. In “Optimization of Digital Audio for Internet Transmission” (May 1998), Mat Hans describes the AudioPak lossless audio coder. This coder combines four low-order linear predictors (0, 1st, 2nd, and 3rd order), each having fixed prediction weights corresponding to known polynomials, with Golomb coding. Use of very low order predictors with fixed predictor weights results in a very simple algorithm with low complexity, but at the expense of lower prediction gain and larger file sizes.
In U.S. Pat. No. 5,839,100, Wegener describes a lossless audio coder that may be used in the MUSICompress system. The Wegener method uses decimation (i.e., selection of every Nth sample) to implement non-linear time domain prediction of an audio signal which is combined with Huffman coding. Decimation introduces aliasing into the predicted signal whereby signal components at the same modulo N frequency are summed. This may distort the signal in a way that prevents accurate prediction of all frequency components, causing lower compression rates.
A paper titled, “SHORTEN: Simple lossless and near-lossless waveform compression”, by Tony Robinson (December 1994) and U.S. Pat. No. 6,041,302 by Bruekers describe a lossless

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