Spectral Sound Gap Filling

We present a new method for automatically filling in gaps of textural sounds. Our approach is to transform the signal to the time-frequency space, fill in the gap, and apply the inverse transform to reconstruct the result. The complex spectrogram of the signal is partitioned into separate overlapping frequency bands. Each band is fragmented by segmentation of the time-frequency space and a partition of the spectrogram in time, and filled in with complex fragments by example. We demonstrate our method by filling in gaps of various types of textural sounds

Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex

model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex. J Neurophysiol 90: 3663–3678, 2003. First published August 27, 2003; 10.1152/jn.00654.2003. We characterize primary auditory cortex (AI) units using a neural model for the detection of frequency and amplitude transitions. The model is a generalization of a model for the detection of amplitude transition. A set of neurons, tuned in the spectrotemporal domain, is created by means of neural delays and frequency filtering. The sensitivity of the model to frequency and amplitude transitions is achieved by applying a 2-dimensional rotatable receptive field to the set of spectrotemporally tuned neurons. We evaluated the model using data recorded in AI of anesthetized ferrets. We show that the model is able to fit the responses of AI units to variety of stimuli, including single tones, delayed 2-tone stimuli and various frequency-modulated tones, using only a small number of parameters. Furthermore, we show that the topographical order in maps of the model parameters is higher than in maps created from response indices extracted directly from the responses to any single stimulus. These results suggest a possible ordered organization of a simple rotatable spectrotemporal receptive field in the mammalian AI

Auditory edge detection: A neural model for physiological and psychoacoustical responses to amplitude transients

edge detection: a neural model for physiological and psychoacoustical responses to amplitude transients. J Neurophysiol 85: 2303–2323, 2001. Primary segmentation of visual scenes is based on spatiotemporal edges that are presumably detected by neurons throughout the visual system. In contrast, the way in which the auditory system decomposes complex auditory scenes is substantially less clear. There is diverse physiological and psychophysical evidence for the sensitivity of the auditory system to amplitude transients, which can be considered as a partial analogue to visual spatiotemporal edges. However, there is currently no theoretical framework in which these phenomena can be associated or related to the perceptual task of auditory source segregation. We propose a neural model for an auditory temporal edge detector, whose underlying principles are similar to classical visual edge detector models. Our main result is that this model reproduces published physiological responses to amplitude transients collected at multiple levels of the auditory pathways using a variety of experimental procedures. Moreover, the model successfully predicts physiological responses to a new set of amplitude transients, collected in cat primary auditory cortex and medial geniculate body. Additionally, the model reproduces several published psychoacoustical responses to amplitude transients as well as the psychoacoustical data for amplitude edge detection reported here for the first time. These results support the hypothesis that the response of auditory neurons to amplitude transients is the correlate of psychoacoustical edge detection

A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise

Psychophysical data suggest that temporal modulations of stimulus amplitude envelopes play a prominent role in the perceptual segregation of concurrent sounds. In particular, the detection of an unmodulated signal can be significantly improved by adding amplitude modulation to the spectral envelope of a competing masking noise. This perceptual phenomenon is known as “Comodulation Masking Release ” (CMR). Despite the obvious influence of temporal structure on the perception of complex auditory scenes, the physiological mechanisms that contribute to CMR and auditory streaming are not well known. A recent physiological study by Nelken and colleagues has demonstrated an enhanced cortical representation of auditory signals in modulated noise. Our study evaluates these CMR-like response patterns from the perspective of a hypothetical auditory edge-detection neuron. It is shown that this simple neural model for the detection of amplitude transients can reproduce not only the physiological data of Nelken et al., but also, in light of previous results, a variety of physiological and psychoacoustical phenomena that are related to the perceptual segregation of concurrent sounds.