Editing Data compression (section)

===== Coding methods =====
To determine what information in an audio signal is perceptually irrelevant, most lossy compression algorithms use transforms such as the [[modified discrete cosine transform]] (MDCT) to convert [[time domain]] sampled waveforms into a transform domain, typically the [[frequency domain]]. Once transformed, component frequencies can be prioritized according to how audible they are. Audibility of spectral components is assessed using the [[absolute threshold of hearing]] and the principles of [[simultaneous masking]]—the phenomenon wherein a signal is masked by another signal separated by frequency—and, in some cases, [[temporal masking]]—where a signal is masked by another signal separated by time. [[Equal-loudness contour]]s may also be used to weigh the perceptual importance of components. Models of the human ear-brain combination incorporating such effects are often called [[psychoacoustic model]]s.<ref name="faxin47"/>

Other types of lossy compressors, such as the [[linear predictive coding]] (LPC) used with speech, are source-based coders. LPC uses a model of the human vocal tract to analyze speech sounds and infer the parameters used by the model to produce them moment to moment. These changing parameters are transmitted or stored and used to drive another model in the decoder which reproduces the sound.

Lossy formats are often used for the distribution of streaming audio or interactive communication (such as in cell phone networks). In such applications, the data must be decompressed as the data flows, rather than after the entire data stream has been transmitted. Not all audio codecs can be used for streaming applications.<ref name="Jaiswal"/>

[[Latency (engineering)|Latency]] is introduced by the methods used to encode and decode the data. Some codecs will analyze a longer segment, called a ''frame'', of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time to decode. The inherent latency of the coding algorithm can be critical; for example, when there is a two-way transmission of data, such as with a telephone conversation, significant delays may seriously degrade the perceived quality.

In contrast to the speed of compression, which is proportional to the number of operations required by the algorithm, here latency refers to the number of samples that must be analyzed before a block of audio is processed. In the minimum case, latency is zero samples (e.g., if the coder/decoder simply reduces the number of bits used to quantize the signal). Time domain algorithms such as LPC also often have low latencies, hence their popularity in speech coding for telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed to implement a psychoacoustic model in the frequency domain, and latency is on the order of 23&nbsp;ms.