## 6 Conclusions  
This work has presented an up-to-date review of the most relevant audio feature extraction techniques related to machine hearing which have been developed for the analysis of speech, music and environmental sounds. With the aim of providing a self-contained reference for audio analysis applications practitioners, this review covers the most elementary and classic approaches to audio feature extraction, dating back to the 1970s, through to the most recent contributions for the derivation of audio features based on new domains of computation and bio-inspired paradigms. To that effect, we revisit classic audio feature extraction techniques taking the complete work by [PERSON] _et al._[17] as a reference, and extend those approaches by accounting for the latest advances in this research field. Besides extending that review with features computed on time, frequency and cepstral domains, we describe feature extraction techniques computed on the wavelet and image domains, obtained from multilinear or non-linear parameterizations, together with those derived from specific representations such as the machine-pursuit algorithm or the Hurst parameterization. Moreover, it is worth noting that a significant number of novel bio-inspired proposals are also described (e.g., including an auditory model such as Mel and Gammatone filter-banks, or derived from the computation of the autocorrelation function or the auditory image model). The described audio features extraction techniques are classified depending on whether they have a physical or a perceptual basis. It is worth mentioning that the increase of complexity in the field of audio parameterization, specifically as regards the more recent perceptual and bio-inspired approaches, makes it difficult to obtain a clear taxonomy that accommodates all the proposals found in the literature. For instance, a different perspective that goes beyond the proposed taxonomy of audio features could be applied to organize some of the described perceptual features. Concretely, many perceptual features are based on obtaining a first set of features that try to emulate the physical resonance system in the cochlea using filterbanks with specific frequency positions and bandwidths (e.g., image domain perceptual features, perceptual filter banks-based cepstral features, auditory saliency maps, some of the wavelet-based perceptual features, or most of the modulation-based perceptual frequency features). Moreover, some of these perceptually-based approaches define a second stage to obtain a set of meaningful features that correlate in some sense with psycho-acoustical responses from the previous spectrogram-based representation. In some cases, this second-stage features are derived from kernel-based 2D processing (like in the wavelet-based perceptual features) while other approaches propose more elaborate processing stages (e.g., auditory saliency maps). Furthermore, the description of the main concepts and principles behind all reviewed feature extraction techniques has considered the specific particularities of the three main types of audio inputs considered: speech, music and environmental sounds. Furthermore, we have included some classic and recent examples to illustrate the application of these techniques in several specific machine hearing related problems, e.g., for speech: segmentation, recognition, speaker verification/identification or language identification; for music: annotation, recommendation, genre classification, instrument recognition, song identification, or mood classification; for environmental sound: recognition, classification, audio-based surveillance or computational auditory scene analysis, among others. Finally, we would like to note that this work has been written not as a thorough collection of _all_ existing audio features extraction techniques related to audio analysis, but as an attempt to collate up-to-date approaches found in the literature of this dynamic field of research.