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The bit layout of the audio data (excluding metadata) is called the audio coding format and can be uncompressed, or compressed to reduce the file size, often using lossy compression. The data can be a raw bitstream in an audio coding format, but it is usually embedded in a container format or an audio data format with defined storage layer.
A lossless audio coding format reduces the total data needed to represent a sound but can be de-coded to its original, uncompressed form. A lossy audio coding format additionally reduces the bit resolution of the sound on top of compression, which results in far less data at the cost of irretrievably lost information.
For example, MP3 and AAC dominate the personal audio market in terms of market share, though many other formats are comparably well suited to fill this role from a purely technical standpoint. First public release date is first of either specification publishing or source releasing, or in the case of closed-specification, closed-source codecs ...
Linear pulse-code modulation (LPCM, generally only described as PCM) is the format for uncompressed audio in media files and it is also the standard for CD-DA; note that in computers, LPCM is usually stored in container formats such as WAV, AIFF, or AU, or as raw audio format, although not technically necessary.
FLAC (/ f l æ k /; Free Lossless Audio Codec) is an audio coding format for lossless compression of digital audio, developed by the Xiph.Org Foundation, and is also the name of the free software project producing the FLAC tools, the reference software package that includes a codec implementation.
Lossless codecs use curve fitting or linear prediction as a basis for estimating the signal. Parameters describing the estimation and the difference between the estimation and the actual signal are coded separately. [49] A number of lossless audio compression formats exist. See list of lossless codecs for a listing.
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Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.