Audio compression (data): Difference between revisions

Revision as of 15:04, 19 April 2004

Revision as of 01:23, 20 April 2004

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First, the vast majority of sound recordings are natural sounds, recorded from the real world, and such data doesn't compress well. In a similar manner, [[photo]]s compress less efficiently with lossless methods than computer-generated images do. But worse, even computer generated sounds can contain very complicated [[waveform]]s that present a challenge to many compression algorithms. This is due to the nature of audio waveforms, which are generally difficult to simplify without a (necessarily lossy) conversion to frequency information, as performed by the human ear.

The second reason is that values of audio [[sample (signal)|sample]]s change very quickly, so generic data compression [[algorithm]]s don't work well for audio, and strings of consecutive bytes don't generally appear very often. However, [[convolution]] with the filter [-1 1] (that is, taking the first difference) tends to [[white noise|whiten]] the spectrum a bit and allows traditional lossless compression to do its job; integration restores the original signal. More advanced codecs such as Shorten ([[SHN]]) and [[FLAC]] use [[linear prediction]] to come up with an optimal whitening filter.

== Examples ==