Abstract
Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.
Highlights
Endowing machines with sensing capabilities similar to those of humans is a long pursued goal in several engineering and computer science disciplines.Ideally, we would like machines and computers to be aware of their immediate surroundings as human beings are
As defined by Mitrović et al [17], this feature is a two-dimensional representation of acoustic versus modulation frequency that is built upon a specific loudness sensation, and it is obtained by Fourier analysis of the critical bands over time and incorporating a weighting stage that is inspired by the human auditory system
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
Summary
Endowing machines with sensing capabilities similar to those of humans (such as vision, hearing, touch, smell and taste) is a long pursued goal in several engineering and computer science disciplines. As the reader may have deduced, machine hearing is an extremely complex and daunting task given the wide diversity of possible audio inputs and application scenarios For this reason, it is typically subdivided into smaller subproblems, and most research efforts are focused on solving simpler, more specific tasks. 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 These non-speech nor music related sounds (hereafter denoted as environmental sounds) should be detectable and recognizable by hearing machines as individual events (Chu et al [14]). 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.
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