in the development of machine hearing systems, including those targeting specific tasks such as artist and song identification ([PERSON] [8]), genre classification ([PERSON] _et al._[9]), instrument recognition ([PERSON] _et al._[10], [PERSON] and [PERSON] [11]), mood classification ([PERSON] _et al._[12]) or music annotation and recommendation ([PERSON] [13]). Thus, speech and music, which up to now have been by far the most extensively studied types of sound sources in the context of machine hearing, present several particular rather unique characteristics. In contrast, other kind of sound sources coming from our environment (e.g., traffic noise, sounds from animals in the nature, _etc._) do not exhibit such particularities, or at least not in such in a clear way. Nevertheless, these non-speech nor music related sounds (hereafter denoted as environmental sounds) should be also detectable and recognizable by hearing machines as individual events ([PERSON] _et al._[14]) or as acoustic scenes ([PERSON] and [PERSON] [15]) (the latter can also be found in the literature denoted as soundscapes, as in the work by [PERSON] [16]). Regardless of its specific goal, any machine hearing system requires performing an in-depth analysis of the incoming audio signal, aiming at making the most of its particular characteristics. This analysis starts with the extraction of appropriate parameters of the audio signal that inform about its most significant traits, a process that usually goes by the name of _audio feature extraction_. Logically, extracting the right features from an audio signal is a key issue to guarantee the success of machine hearing applications. Indeed, the extracted features should provide a compact yet descriptive vision of the parametrized signal, highlighting those signal characteristics that are most useful to accomplish the task at hand, be it detection, identification, classification, indexing, retrieval or recognition. And of course, depending on the nature of the signal (_i.e._, speech, music or environmental sound) and the targeted application, it will be more interesting that these extracted features reflect the characteristics of the signal from a physical or perceptual point of view. This paper presents an up-to-date state-of-the-art review of the main audio feature extraction techniques applied to machine hearing. We build on the complete review about features for audio retrieval by [PERSON] _et al._[17], and we have included the classic approaches in that work for the sake of completeness. In addition, we present the latest advances on audio feature extraction techniques together with new examples of their application to the analysis of speech, music and environmental sounds. It is worth noting that most of the recently developed audio feature techniques introduced in the last decade have entailed the definition of new approaches of analysis beyond the classic domains (_i.e._, temporal, frequency-based and cepstral), such as the ones developed on the wavelet domain, besides introducing image-based and multilinear or non-linear representations, together with a significant increase of bio-inspired proposals. This paper is organized as follows. Section 2 describes the main constituting blocks of any machine hearing system, focusing the attention on the audio feature extraction process. Moreover, given the importance of relating the nature of the signal with the type of extracted features, we detail the primary characteristics of the three most frequent types of signals involved in machine hearing applications: speech, music and environmental sounds. Next, Section 3 describes the followed taxonomy to describe both classic and recently defined audio feature extraction techniques. Next, the description of the rationale and main principles of approaches that are based on the physical characteristics of the audio signal are described in Section 4, while those that try to somehow include perception in the parameterization process are explained in Section 5.