Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory perception and computation <s> The theory of statistical communication provides an invaluable framework within which it is possible to formulate design criteria and actually obtain solutions for digital filters. These are then applicable in a wide range of geophysical problems. The basic model for the filtering process considered here consists of an input signal, a desired output signal, and an actual output signal. If one minimizes the energy or power existing in the difference between desired and actual filter outputs, it becomes possible to solve for the so-called optimum, or least squares filter, commonly known as the “Wiener” filter. In this paper we derive from basic principles the theory leading to such filters. The analysis is carried out in the time domain in discrete form. We propose a model of a seismic trace in terms of a statistical communication system. This model trace is the sum of a signal time series plus a noise time series. If we assume that estimates of the signal shape and of the noise autocorrelation are available, we may calculate Wiener filters which will attenuate the noise and sharpen the signal. The net result of these operations can then in general be expected to increase seismic resolution. We show a few numerical examples to illustrate the model's applicability to situations one might find in practice. <s> BIB001 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory perception and computation <s> An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. > <s> BIB002 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory perception and computation <s> We propose a method for estimating the prior signal-to-noise ratio (SNR), which is used for calculating the Wiener filter for distant sound source extraction, from output signals of beamforming using statistical mapping based on the deep neural network (DNN). Since informative features to estimate the prior SNR are included in multiple beamforming outputs, the SNR can be accurately estimated by this mapping using the DNN. The proposed method was applied to a large microphone array, the design of which was optimized to form effective directivity patterns to extract distant sound sources. Experimental results proved that the target source was clearly extracted with the proposed method. <s> BIB003
Auditory perception is a central pathway for information interaction in human beings. It usually occurs within a complicated auditory setting, which includes multiple sound sources and reverberation. However, machine auditory perception shows a significantly lower performance in actual environments. To reduce the detrimental effects caused by competing sound sources, signal processing algorithms for speech enhancement based on the input of a single microphone were studied in the 1960s, and they have achieved a good performance for near-field recordings but were useless for far-field recordings BIB001 . In the 1970s, algorithms based on microphone arrays were studied to detect and enhance target sounds in far-field situations, but they usually worked well only for high signal-tonoise ratio (SNR) scenarios and, critically, they required identical microphones for recording BIB002 . With the recent development in CNNs, some algorithms have helped enhance target sound sources in a reverberant environment, and the limitation of identical microphones was partially relaxed BIB003 . However, they were still not efficient for settings with multiple sound sources. The auditory mechanism of binaural processing has revealed that the physical structure of the human ear and body is important for sound localization, and this information is conveyed by the human head related transfer function (HRTF). A recent work using CNN to demodulate this function showed a promising result for multiple sound source settings and low SNR  . Nevertheless, the question of how to develop effective auditory perception and computation algorithms in complicated auditory settings remains open, and it should be addressed in future research in the era of AI 2.0.