Just as the visual system parses complex scenes into identifiable objects, the auditory system must organize sound elements scattered in frequency and time into coherent “streams”. Current neuro-computational theories of auditory streaming rely on tonotopic organization of the auditory system to explain the observation that sequential spectrally distant sound elements tend to form separate perceptual streams. Here, we show that spectral components that are well separated in frequency are no longer heard as separate streams if presented synchronously rather than consecutively. In contrast, responses from neurons in primary auditory cortex of ferrets show that both synchronous and asynchronous tone sequences produce comparably segregated responses along the tonotopic axis. The results argue against tonotopic separation per se as a neural correlate of stream segregation. Instead we propose a computational model of stream segregation that can account for the data by using temporal coherence as the primary criterion for predicting stream formation.
Humans and other animals can attend to one of multiple sounds, and follow it selectively over time. The neural underpinnings of this perceptual feat remain mysterious. Some studies have concluded that sounds are heard as separate streams when they activate well-separated populations of central auditory neurons, and that this process is largely pre-attentive. Here, we propose instead that stream formation depends primarily on temporal coherence between responses that encode various features of a sound source. Furthermore, we postulate that only when attention is directed toward a particular feature (e.g., pitch or location) do all other temporally coherent features of that source (e.g., timbre and location) become bound together as a stream that is segregated from the incoherent features of other sources. Experimental neurophysiological evidence in support of this hypothesis will be presented. The focus, however, will be on a computational realization of this idea and a discussion of the insights learned from simulations to disentangle complex sound sources such as speech and music. The model consists of a representational stage of early and cortical auditory processing that creates a multidimensional depiction of various sound attributes such as pitch, location, and spectral resolution. The following stage computes a coherence matrix that summarizes the pair-wise correlations between all channels making up the cortical representation. Finally, the perceived segregated streams are extracted by decomposing the coherence matrix into its uncorrelated components. Questions raised by the model are discussed, especially on the role of attention in streaming and the search for further neural correlates of streaming percepts.
An important aspect of the analysis of auditory “scenes” relates to the perceptual organization of sound sequences into auditory “streams.” In this study, we adapted two auditory perception tasks, used in recent human psychophysical studies, to obtain behavioral measures of auditory streaming in ferrets (Mustela putorius). One task involved the detection of shifts in the frequency of tones within an alternating tone sequence. The other task involved the detection of a stream of regularly repeating target tones embedded within a randomly varying multitone background. In both tasks, performance was measured as a function of various stimulus parameters, which previous psychophysical studies in humans have shown to influence auditory streaming. Ferret performance in the two tasks was found to vary as a function of these parameters in a way that is qualitatively consistent with the human data. These results suggest that auditory streaming occurs in ferrets, and that the two tasks described here may provide a valuable tool in future behavioral and neurophysiological studies of the phenomenon.
Distance education is generally developed through live broadcast or video playback. Because of the influence of various factors in the process of distance education, the pixel characteristics in the original educational resource image will change. Based on this, this study is based on digital image processing technology to correct the distance education image. In addition, this study uses the layered processing model to decompose each color channel and then process the image brightness channel, which effectively reduces the computational overhead while ensuring the fusion effect. Finally, combined with comparative experimental research, the performance analysis of the algorithm is carried out. Research shows that the algorithm of this study has good performance in image correction and can provide theoretical reference for subsequent related research.
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