## 5 Perceptual Audio Features Extraction Techniques
### Other Domains  
The literature contains other approaches to perceptual audio feature extraction that operate on domains different to the ones just reviewed. Some of the most significant are the eigenspace-basedfeatures, the electroencephalogram-based features and the auditory saliency map. The following paragraphs briefly describe these approaches.  
* **Eigenspace-based features:** in this category, we find Rate-scale-frequency (RSF) features, which describe modulation components present in certain frequency bands of the auditory spectrum, and they are based in the same human auditory model that incorporates the noise-robust audio features (NRAF), as described in the work by [PERSON] _et al._[228] (see Section 5.6.1). RFS represent a compact and decorrelated representation (they are derived performing a Principal Component Analysis stage) of the two-dimensional Wavelet transform applied to the audio spectrum;
* **Electroencephalogram-based features:** or EEG-based features for short, these find application in human-centered favorite music estimation, as introduced by [PERSON] _et al._[244]. In that work, the authors compute features from the EEG signals of a user that is listening to his/her favorite music, while simultaneously computing several features from the audio signal (root mean square, brightness, ZCR or tempo, among others). Subsequently, both types of features are correlated by means of kernel canonical correlation analysis (KCCA), which allows deriving a projection between the audio features space and the EEG-based feature space. By using the obtained projection, the new EEG-based audio features can be derived from audio features, since this projection provides the best correlation between both feature spaces. As a result, it becomes possible to transform original audio features into EEG-based audio features with no need of further EEG signals acquisition.
* **Auditory saliency map:** is a bottom-up auditory attention model which computes an auditory saliency map from the input sound derived by [PERSON] _et al._[245; 246], and it has been applied to environmental sounds in the work by [PERSON] and [PERSON][247] (perception of transportation noise). The saliency map holds non-negative values and its maximum defines the most salient location in 2D auditory spectrum. First, auditory spectrum of sound is estimated using an early auditory (EA) system model, consisting of cochlear filtering, inner hair cell (IHC), and lateral inhibitory stages mimicking the process from basilar membrane to the cochlear nucleus in the auditory system (using a set of constant-Q asymmetric band-pass filters uniformly distributed along a logarithmic frequency axis). Next, the auditory spectrum is analyzed by extracting a set of multi-scale features (2D spectro-temporal receptive filters) which consist of intensity, frequency contrast, temporal contrast and orientation feature channels. Subsequently, center-surround differences (point wise differences across different center-based and surrounding-based scales) are calculated from the previous feature channels, resulting in feature maps. From the computed 30 features maps (six for each intensity, frequency contrast, temporal contrast and twelve for orientation) an iterative and nonlinear normalization algorithm (simulating competition between the neighboring salient locations using a large 2D difference of Gaussians filter) is applied to the possible noisy feature maps, obtaining reduced sparse representations of only those locations which strongly stand-out from their surroundings. All normalized maps are then summed to provide bottom-up input to the saliency map.