## 5 Perceptual Audio Features Extraction Techniques
### Wavelet-Based Perceptual Features  
In the following paragraphs, we describe the most commonly used Wavelet-based perceptual frequency features, which represent an extension of the wavelet-based physical features previously reviewed in Section 4.3.  
* **Kernel Power Flow Orientation Coefficients (KFPOC):** in the works by [PERSON] and [PERSON] [184; 185], a bank of 2D kernels is used to estimate the orientation of the power flow at every point in the auditory spectrogram calculated using a Gammatone filter bank ([PERSON] and [PERSON] [161]), obtaining an ASR front-end with increased robustness to both noise and room reverberation with respect to previous approaches, and specially for small vocabulary tasks.
* **Mel Frequency Discrete Wavelet Coefficients**: or MFDWC for short, account for the perceptual response of the ear by applying the discrete WT to the Mel-scaled log filter bank energies obtained from the input signal (see [PERSON] and [PERSON] [186]). MFDWC, which were initially defined to improve speech recognition problems ([PERSON] _et al._[187]), have been subsequently applied to other machine hearing realed applications such as speaker verification/identification (see [PERSON] and [PERSON] [188], [PERSON] _et al._[189]), and audio-based surveillance systems ([PERSON] _et al._[24]).
* **Gammatone wavelet features:** is a subtype of audio features formulated in the Wavelet domain that accounts for perceptual modelling is the Gammatone Wavelet features (GTW) (see [PERSON] and [PERSON] [190], [PERSON] _et al._[191]). These features are obtained by replacing typical mother functions, such as Morlet ([PERSON] _et al._[192]), Coiflet ([PERSON] [193]) or Daubechies [194]) by Gammatone functions, which model the auditory system. GTW features show superior classification accuracy both in noiseless and noisy conditions when compared to Daubechies Wavelet features in classification of surveillance-related sounds, as exposed by [PERSON] and [PERSON] [190].
* **Perceptual wavelet packets:** the Wavelet packet transform is an implementation version of the discrete WT, where the filtering process is iterated on both the low frequency and high frequency components (see [PERSON] _et al._[195]), which has been optimized perceptually by including the representation of the input audio into critical bands described by [PERSON] [196] and [PERSON] _et al._[197]. Wavelet packet transform has been used in different applications like in the work by [PERSON] _et al._[198] for audio coding purposes, or in audio watermarking ([PERSON] [199]). Perceptual Wavelet Packets (PWP) have been applied to bio-acoustic signal enhancement ([PERSON] _et al._[197]), speech recognition ([PERSON] _et al._[200]), and more recently also for baby crying sound events recognition ([PERSON] [201]).
* **Gabor functions:** the work by [PERSON] models the receptive field of cortical neurons also as two-dimensional complex Gabor function [202]. More recently, [PERSON] _et al._ have studied in [203] the use of Gabor functions against learning the features via Independent Component Analysis technique for the computation of local features in a two-layer hierarchical bio-inspired approach. [PERSON] _et al._ employ two-dimensional Gabor functions with different scales and directionsto analyze the localized patches of the power spectrogram [204], improving the speech recognition performance in noisy environments, compared with other previous speech feature extraction methods. In a similar way, [PERSON] _et al._[205] propose an optimization of spectro-temporal Gabor filterbank features for the audio events detection task. In [206,207], [PERSON] and [PERSON] describe a new way of deriving the Gabor filters as a particular case (using non-causal Gaussian windows) of frequency selective temporal receptive fields, representing the first layer of their scale-space theory for auditory signals.