# Psychaocoustic Models for Perceptual Audio Coding--A Tutorial Review
## 2 Monaural Perceptual Effects and Models
### High Efficiency Models for Perceptual Audio Coding  
The previous sections described the classic codec implementation structure with separate modules for perceptual (psychoacoustic) model and quantization or coding. In practice, there are successful codec implementations that grossly depart from the concept of a distinct perceptual model attempting to match psychoacoustic phenomena as accurately as possible. A prominent example is the so-called _FastEnc_ implementation of the _High Efficiency v2_ (HE-AAC v2) codec, as it has become ubiquitous through its standardization within 3 rd Generation Partnership Project 3 GPP [39] and its widespread deployment on virtually all existing mobile phones. Compared to the classic implementations (which historically computed the final codec output from a computationally complex nested two-loop iterative algorithm [38]), the FastEnc implementation is faster in execution by a factor of up to 15 (e.g., for MP3 at 128 kbit/s), while providing at least the same subjective quality at each given bitrate.  
A closer look at the implementation reveals some interesting aspects:  
* The psychoacoustic model is calculated directly in the critically sampled codec filterbank (i.e., a Modified Discrete Cosine Transform--MDCT [40]) domain. This omits the additional computational complexity of calculating a dedicated DFT-based filterbank for the psychoacoustic model.
* The threshold calculation is based on few very basic assumptions that represent a simple worst-case masking threshold. Initially, a (very conservative) Signal-to-Mask Ratio (SMR) of 29 dB is assumed, i.e., a minimum masking ability that is satisfied by all types of signals, be they of tonal or noise-like nature. Consequently, no tonality is computed or taken into consideration. Modeling of inter-band masking uses fixed slopes of +30 dB/Bark (lower slope) and \(-\)15 dB/Bark (upper slope). Furthermore, the raw thresholds are adjusted by a pre-echo control stage.
* Subsequent to the initial threshold calculation, a threshold adaption stage adjusts the overly conservative thresholds to fit into the available bit budget. The amount of threshold reduction needed is estimated from a simplified estimate of the required bitrate, based on the signal spectrum and associated computed masking threshold--the so-called _Perceptual Entropy_, or _PE_[35]. The final quantizer setting (scale factors) are computed directly from the threshold values rather than determined by an iterative procedure. For improved perceptual performance, the introduction of holes into the signal spectrum by too coarse quantization (\"birdies\" [10]) is avoided by guaranteeing a minimum spectral precision in each relevant frequency band.  
In summary, the described model represents a state-of-the-art way of implementing a perceptual model as part of a codec that is designed to be highly attractive in both computational complexity and subjective audio quality. To this end, the original reference coder design using a complex iterative encoding procedure has been replaced essentially by a one-shot estimation process that includes many aspects of numerical optimization, which are beyond the scope of this article.