## 3 Audio Features Taxonomy and Review of Extraction Techniques  
There exists a myriad of approaches to extract significant features from the audio input of a machine hearing system. On the one hand, we can find those approaches that are only devoted to extract physical features of the audio input. These extraction techniques differ on the domain of operation, ranging from the classic time, frequency or cepstral domains to the derivation of features based on other recent representations. Specifically, speech, music and environmental sounds typically present rich time-varying characteristics with very diverse contents (as shown in Figure 3), which can be parameterized in that domain, e.g., by computing from the analyzed input frame the sign-change rate, the fundamental periodicity, the signal power or amplitude, _etc._ Moreover, the dynamic variations of those audio signals can present relevant information in a transformed domain, e.g., through a Fourier transform (see Figure 4), in the cepstral or Wavelet domains, or from eigenspaces or even through non-linear representations, from which specific features related to e.g., spectrum, harmonicity, line prediction or phase-space can be extracted.  
On the other hand, we can find those techniques that try to explicitly integrate perception in the parameterization process or derive it through the computation of signal features capable of extracting perceptually relevant aspects from the input audio, as described by [PERSON] _et al._[31]. The former typically include in the parameterization process simplified audition models of the hearing system (e.g., by considering from Bark, Mel or Gammatone filter-banks to more complex models based on electroencephalograms). This bio-inspired approach has to take into account the target species of the machine hearing system, being adapted to the cochlear response of that species, e.g., human beings or animals (see the work by [PERSON] _et al._[32; 33]). The latter approach to embed perception during the feature extraction process is based on the computation of low-level features that somehow explain a high-level sensation of sound similarity, which has been validated perceptually ([PERSON] _et al._[31]), such the ones related to temporal or frequency-based domains (e.g., loudness, pitch, rhythm, _etc._), or the ones derived from the computation of the autocorrelation function and the auditory image model for example.  
In this work, we organize the review of the most relevant and recent audio feature extracting techniques found in the literature following the hierarchical taxonomy depicted in Figure 5. This taxonomy builds on the one introduced in the review by [PERSON] _et al._[17]. We fist classify the techniques by differentiating physically-based approaches from those with a perceptual basis, and subsequently dividing them according to the domain of parameterization: time, frequency, wavelet, image-based, cepstral, or other domains.  
It is important to highlight that the main goal of this paper is to provide the reader with a broad view of the existing approaches to audio feature extraction. The detailed mathematical analysis and critical comparison between features lies beyond the scope and objectives of our work. The reader interested in a mathematical description of audio features is referred to the works by [PERSON] [34] and by [PERSON] and [PERSON] [35]. Additionally, comparisons between several types of features can be found in other works. Some of these works are focused on comparing the performance of several features in the context of different machine hearing applications, such as sound recognition [36] or music retrieval [37]. Finally, the work by [PERSON] and [PERSON] [38] presents a comparison of characteristics of audio features, such as their robustness to noise and spectro-temporal detail.