In this context the encoded data is the losslessly compressed data stream, while the decoded data is the recovered original audio waveform.
9.2. The Deflate Compression Algorithm
In this chapter we use compression and encoding interchangeably. Common lossless compressors such as WinZIP and StuffiT are used on arbitrary computer data files and usually provide compressed files roughly half the size of the original. However, the compression model e. Nonetheless, it is important to remember that there are no guarantees about the amount of compression obtainable from an arbitrary audio data file: It is even possible that the compressed file ends up larger than the original due to the overhead of data packing information!
Clearly the lossless algorithm should detect this situation and not "compress" that file. To examine this question, consider the audio signal excerpt displayed in Fig. This excerpt is approximately 5 s of audio taken from a bit PCM data file with kHz sample rate 1. Figure An enlarged portion of the signal in Fig.
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The Ziv-Lempel methods take advantage of patterns that occur repeatedly in the data stream. Note that even if the audio waveform is roughly periodic and consistent as in Fig.
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This property of audio signals makes them quite different statistically from structured data such as English text or computer program source code, and therefore we should not be surprised that different compression strategies are required. Amplitude Range and Segmentation One characteristic of this signal is that its amplitude envelope local peak value over a block of samples varies with time.
In fact, a substantial portion of the excerpt has its amplitude envelope below one-quarter of the full-scale value. In this case we know that at least the 2 most significant bits MSBs of the PCM word will not vary from sample to sample, other than for changes in sign assuming 2's complement numerical representation. If we designed an algorithm to identify the portions of the signal with low amplitude, we could obtain a reduction in bit rate by placing a special symbol in the data stream to indicate that the n most significant bits are the same for each of the following samples, and thus we can eliminate the redundant n MSBs from each subsequent sample .
We could also go further by indicating somehow that several samples in a row would share the same sign.
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Of course, we would need to monitor the signal to detect when the low-amplitude condition was violated, so in addition to the "same MSB" symbol we might also choose to include an integer indicating the number of samples, or block size, for which the condition remained valid. Thus, we could expect to get a reduction in bit rate by segmentation of the signal into blocks where the signal's amplitude envelope was substantially less than full scale.
It is clear, of course, that the amount of compression will depend upon how frequently low-amplitude blocks occur in the signal. We can also examine another amplitude-related feature of audio data. As mentioned above, the audio excerpt shown in Figs. However, if we look carefully at the data for this example, we can discover another heuristic strategy for data compression .
Some of the audio data sample values taken from near the beginning of the segment shown in Fig. In fact, this group of 16 samples is low enough in amplitude that the 6 most significant bits can be replaced by a single bit indicating the sign of the number, giving a tidy bit rate reduction of 5 bits per sample. Looking further, notice that even though the data file is stored with bit resolution, the actual data samples do not contain information in the 8 least significant bits LSBs.
This is because the original data stream was actually obtained from a digital audio tape that included only the standard bit per sample audio resolution, and thus the 8 LSBs are filled with zeros. Again, we can obtain significant data compression merely by storing a special format code that identified the m LSBs 8 in this case as being redundant for the file and not actually store them. The decoder would detect the special format code and reinsert the m LSBs in the decoded output file.
Although such a distinct situation of excess data resolution might seem contrived - and we certainly cannot count on this peculiarity in all data files - it is mentioned here as an example of some of the special circumstances that can be detected to aid in lossless audio compression. Part 2 of this article will continue its focus on the key principles of lossless audio data compression - including multiple-channel redundancy, prediction, and entropy coding - as well as discuss some practical system design issues.
Perceptual coding of high quality digital audio. Kahrs and K. Brandenburg, Eds. Craven, P.
Lossless coding for audio discs. Journal of the Audio Engineering Society , Vol. Gerzon, M. The MLP lossless compression system. Stock photo. Pre-owned: lowest price The lowest-priced item that has been used or worn previously. Hardcover in Good condition They are not actual photos of the physical item for sale and should not be relied upon as a basis for edition or condition.
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Add to cart. Be the first to write a review About this product. About this product Product Information The 21 chapters in this handbook are written by the leading experts in the world on the theory, techniques, applications, and standards surrounding lossless compression. As with most applied technologies, the standards section is of particular importance to practicing design engineers.
In order to create devices and communication systems that can communicate and be compatible with other systems and devices, standards must be followed. Additional Product Features Dewey Edition. Never before has the topic of lossless compression been so topical.
Lossless Compression of Audio Data - Part 1
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