Linear predictive coding of an audio signal

Abstract

An apparatus for linear predictive coding of an audio signal comprises a segmentation processor (201) which generates signal segments for the audio signal. An autocorrelation processor (401) for generates a first autocorrelation sequence for each signal segment and a modification processor (403) generates a second autocorrelation sequence for each signal segment by modifying the first autocorrelation sequence in response to at least one psychoacoustic characteristic. A prediction coefficient processor (405) determines linear predictive coding coefficients for each signal segment in response to the second autocorrelation sequence. The invention allows a low complexity linear encoding which takes into account psychoacoustic considerations thereby allowing an improved perceived coding quality for a given data rate.

Description

technical field [0001] The invention relates to linear predictive coding of audio signals. Background technique [0002] Digital encoding of various source signals has become increasingly important over the past few decades, as the representation and communication of digital signals has gradually replaced analog representation and communication. For example, mobile telephone systems such as the Global System for Mobile Communications are based on digital speech coding. In addition, the distribution of media content such as video and music is also more based on digital content encoding. [0003] In content coding and especially in audio and speech coding, linear predictive coding is a frequently employed tool because it provides high quality for low data rates. Linear predictive coding has traditionally been applied primarily to individual signals, but it can also be used for multi-channel signals, such as stereo audio signals. [0004] Linear predictive coding achieves ef...

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Problems solved by technology

[0007] However, although this method allows the use of linear prediction for audio coding that takes psychoacoustic masking into account, it also has a number of disadvantages

In particular, this method requires the execution of a psychoacoustic model for each signal segment, which is complex and computationally expensive

Furthermore, the method tends to be inflexible and specifically requires the prediction filter to be a warp or Laguerre filter in order to work on psychoacoustically relevant frequency scales

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