## 5 Perceptual Audio Features Extraction Techniques
### Cepstral Domain Perceptual Features
#### 5.6.1 Perceptual Filter Banks-Based Cepstral Features  
Perceptual filter banks-based cepstral features are based on the computation of cepstral-based parameters following an approach based on, firstly, obtaining the logarithm of the magnitude's Fouriertransform, or using an specific filter bank decomposition with some possible perceptual criteria; and secondly, performing a Fourier transform (or Cosine transform) of the previous result. This type of features comprises the well-known Mel Frequency Cepstral Coefficients and their variants, which are often based on using different frequency scales before the last Fourier-based stage. Some examples include Equivalent Rectangular Bandwidths (ERB) (see [PERSON] _et al._[219]), Bark (see [PERSON] [220]), critical bands (as in the work by [PERSON] [196]) and octave-scale (see [PERSON] _et al._[221]. Another aspect connected to these type of features is that they are mostly based on the computation of a _cohleagram_ (the resulting time-frequency output of the filterbanks), which in some sense try to model the frequency selectivity of the cochlea (as in the work by [PERSON] _et al._[31]). The following paragraphs describe the most relevant features in this area. * **Mel Frequency Cepstral Coefficients**: also denoted as MFCC, have been largely employed in the speech recognition field but also in the field of audio content classification (see the work by [PERSON] and [PERSON] [58]), due to the fact that their computation is based on perceptual-based frequency scale in the first stage (the human auditory model in which is inspired the frequency Mel-scale). After obtaining the frame-based Fourier transform, outputs of a Mel-scale filter bank are logarithmized and finally they are decorrelated by means of the Discrete Cosine Transform (DCT). Only first DCT coefficients (usually from 8 to 13) are used to gather information that represents the low frequency component of the signal's spectral envelope (mainly related to timbre). MFCC\({}^{\prime}\)s have been used also for music classification (see the works by [PERSON] _et al._[10], [PERSON] _et al._[41], [PERSON] and [PERSON] [28], or [PERSON] _et al._[9]), singer identification (as in [PERSON] _et al._[145]), environmental sound classification (see [PERSON] and [PERSON] [222], or [PERSON] _et al._[18]), audio-based surveillance systems ([PERSON] _et al._[24]), being also embedded in hearing aids (see [PERSON] and [PERSON] [223]) and even employed detect breath sound as an indicator of respiratory health and disease ([PERSON] _et al._[224]). Also, some particular extensions of MFCC have been introduced in the context of speech recognition and speaker verification in the aim of obtaining more robust spectral representation in the presence of noise (e.g., in the works by [PERSON] and [PERSON] [225], [PERSON] _et al._[27], or [PERSON] [226]). * **Greenwood Function cepstral coefficients:** building on the seminal work by [PERSON] [196], where it was stated that many mammals have a logarithmic cochlear-frequency response, [PERSON] _et al._[32] introduced Greenwood function Cepstral Coefficients (GFCC), extracting the equal loudness curve from species-specific audiogram measurements as an audio feature extraction for the analysis of environmental sound coming from the vocalization of those species. Later, this features were applied also to multichannel speech recognition by [PERSON] _et al._[227]. * **Noise-robust audio features:** or NRAF for short, these features incorporate a specific human auditory model based on a three stage process (a first stage of filtering in the cochlea, transduction of mechanical displacement in electrical activity-log compression in the hair cell stage-, and a reduction stage using decorrelation that mimics the lateral inhibitory network in the cochlear nucleus) (see [PERSON] _et al._[228]).