Advanced Audio Coding - How AAC Works

How AAC Works

AAC is a wideband audio coding algorithm that exploits two primary coding strategies to dramatically reduce the amount of data needed to represent high-quality digital audio.

  1. Signal components that are perceptually irrelevant are discarded;
  2. Redundancies in the coded audio signal are eliminated.

The actual encoding process consists of the following steps:

  • The signal is converted from time-domain to frequency-domain using forward modified discrete cosine transform (MDCT). This is done by using filter banks that take an appropriate number of time samples and convert them to frequency samples.
  • The frequency domain signal is quantized based on a psychoacoustic model and encoded.
  • Internal error correction codes are added;
  • The signal is stored or transmitted.
  • In order to prevent corrupt samples, a modern implementation of the Luhn mod N algorithm is applied to each frame

The MPEG-4 audio standard does not define a single or small set of highly efficient compression schemes but rather a complex toolbox to perform a wide range of operations from low bitrate speech coding to high-quality audio coding and music synthesis.

  • The MPEG-4 audio coding algorithm family spans the range from low bitrate speech encoding (down to 2 kbit/s) to high-quality audio coding (at 64 kbit/s per channel and higher).
  • AAC offers sampling frequencies between 8 kHz and 96 kHz and any number of channels between 1 and 48.
  • In contrast to MP3's hybrid filter bank, AAC uses the modified discrete cosine transform (MDCT) together with the increased window lengths of 1024 or 960 points.

AAC encoders can switch dynamically between a single MDCT block of length 1024 points or 8 blocks of 128 points (or between 960 points and 120 points, respectively).

  • If a signal change or a transient occurs, 8 shorter windows of 128/120 points each are chosen for their better temporal resolution.
  • By default, the longer 1024-point/960-point window is otherwise used because the increased frequency resolution allows for a more sophisticated psychoacoustic model, resulting in improved coding efficiency.

Read more about this topic:  Advanced Audio Coding

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