## 5 Perceptual Audio Features Extraction Techniques
### Image Domain Perceptual Features  
In this section, the image-based audio features introduced in Section 4.4 are extended whenever they include some aspect of psycho-acoustics or perceptual models of hearing. Following a perceptual image-based approach, the main features described in the literature are the following:  
* **Spectrogram image features:** as introduced in Section 4.4, spectrogram image features have been also derived from front-end parametrizations which make use of psychoacoustical models. In [213], [PERSON] _et al._ use GHT to construct a codebook from a Gaussian Mixture Model-Hidden Markov Model based ASR, in order to train an artificial neural network that learns a discriminative weighting for optimizing the classification accuracy in a frame-level phoneme recognition application. In this work MFCC are used as front-end parametrization. The same authors compute in [214] a robust sparse spike coding of the 40-dimension Mel-filtered spectrogram (detection of high energy peaks that correlate with a codebook dictionary) to learn a neural network for sound event classification. The results show a superior reliability when the proposed parameterization is used against the conventional raw spectrogram.
* **Auditory image model:** or AIM for short, this feature extraction technique includes functional and physiological modules to simulate auditory spectral analysis, neural encoding and temporal integration, including new forms of periodicity-sensitive temporal integration that generate stabilized auditory images ([PERSON] _et al._[215; 216]). The encoding process is based on a three stage system. Briefly, the spectral analysis stage converts the sound wave into the model's representation of basilar membrane motion (BMM). The neural encoding stage stabilizes the BMM in level and sharpens features like vowel formants, to produce a simulation of the neural activity pattern produced by the sound in the auditory nerve. The temporal integration stage stabilizes the repeating structure in the NAP and produces a simulation of our perception, referred to as the auditory image.
* **Stabilized auditory image:** based on the AIM features, the stabilized auditory image (SAI) is defined as a two-dimensional representation of the sound signal (see [PERSON] [137]): the first dimension of a SAI frame is simply the spectral dimension added by a previous filterbank analysis, while the second comes from the strobed temporal integration process by which an SAI is generated. SAI has been applied to speech recognition and audio search [137] and more recently, a low-resolution overlapped SIF has been introduced together with Deep Neural Networks (DNN) to perform robust sound event classification in noisy conditions ([PERSON] _et al._[217]).
* **Time-chroma images:** this feature is a two dimensional representation for audio signals that plots the chroma distribution of an audio signal over time, as described in the work by [PERSON] and [PERSON] [218]. This feature employs a modified definition of the chroma concept called chroma, which is defined as the set of all pitches that are apart by \(n\) octaves. Coupled with a fingerprinting algorithm that extracts local fingerprints from the time-chroma image, the proposed feature allows improved accuracy in audio copy detection and song identification.