## 2 Machine Hearing
### Architecture of Machine Hearing Systems  
Regardless of the specific kind of problem addressed, the structure of the underlying system can be described by means of a generic and common architecture design that is depicted in Figure 2.  
In a first stage, the continuous audio stream captured by a microphone is segmented into shorter signal chunks by means of a windowing process. This is achieved by sliding a window function over the theoretically infinite stream of samples of the input signal, and ends up by converting it into a continuous sequence of finite blocks of samples. Thanks to windowing, the system will be capable of operating on sample chunks of finite length. Moreover, depending on the length of the window function, the typically non-stationary audio signal can be assumed to be _quasi-stationary_ within each frame, thus facilitating subsequent signal analysis. The choice of the type and length of the window function, as well as the overlap between consecutive signal frames, is intimately related to the machine hearing application at hand. It seems logical that, for instance, the length of the window function should be proportional to the minimum length of the acoustic events of interest. Therefore, window lengths between 10 and 50 milliseconds are typically employed to process speech or to detect transient noise events [13], while windows of several seconds are used in computational auditory scene analysis (CASA) applications (as in the works by [PERSON] _et al._[18], [PERSON] _et al._[14], [PERSON] and [PERSON] [15], or [PERSON] _et al._[19]). Further discussion about the windowing process and its effect on the windowed signal lies beyond the scope of this work. The interested reader is referred to classic digital signal processing texts (e.g., see the book by [PERSON] and [PERSON] [20]). Once the incoming audio stream has been segmented into finite length chunks, audio features are extracted from each one of them. The goal of feature extraction is to obtain a compact representation of the most salient acoustic characteristics of the signal, converting a \(N\) samples long frame into \(K\) scalar coefficients (with \(K<<N\)), thus attaining a data compaction that allows increasing the efficiency of subsequent processes [13]. To that effect, these features may consider the physical or perceptual impact of signal contents computed in the time, frequency, _etc._ domains. In this sense, modeling the time evolution of audio signals has been found to be of paramount importance when it comes to perform some types of machine hearing tasks, such as the recognition of environmental sounds (as described by [PERSON] [21]) or the identification of rhythmic patterns in music ([PERSON] and [PERSON] [22]) for example. To keep this time information, the features extracted from several subsequent signal frames can be merged into a single feature vector. It should be noted that, due to this feature merging process, the feature vectors acquire a very high dimensionality that may represent a hurdle to the subsequent audio analysis process, with the so-called _curse of dimensionality_ problem, as described by [PERSON] [23]. In order to compact the feature vectors, feature extraction techniques are sometimes followed by a data dimensionality reduction process. To this end, several approaches may be considered: from representing vectors in terms of some of their statistics (as done in the works by [PERSON] _et al._[24] or by [PERSON] [25]) to more complex approaches like analyzing  
Figure 2: Generic architecture of a typical machine hearing system. the principal components of the feature vector ([PERSON] _et al._[26]), thus projecting the data onto a transformed space. And finally, an audio analysis task must be conducted upon the feature vectors obtained in the previous step. Of course, _audio analysis_ is a generic label that tries to encompass any audio processing necessary to tackle the specific machine hearing application at hand.