In-Channel Cancellation
de Cheveigne, A.
Show abstract
A model of early auditory processing is proposed in which each peripheral channel is processed by a delay-and-subtract cancellation filter, tuned independently for each channel with a criterion of minimum power. For a channel dominated by a pure tone or a resolved partial of a complex tone, the optimal delay is its period. For a channel responding to harmonically-related partials, the optimal delay is their common fundamental period. Each peripheral channel is thus split into two subchannels, one that is cancellation-filtered and the other not. Perception can involve either or both, depending on the task. The model is illustrated by applying it to the masking asymmetry between pure tones and narrowband noise: a noise target masked by a tone is more easily detectable than a tone target masked by noise. The model is one of a wider class of models, monaural or binaural, that cancel irrelevant stimulus dimensions so as to attain invariance to competing sources. Similar to occlusion in the visual domain, cancellation yields sensory evidence that is incomplete, thus requiring Bayesian inference of an internal model of the world along the lines of Helmholtzs doctrine of unconscious inference.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Why is the perceptual octave stretched? An account based on mismatched time constants within the auditory brainstem. 98%
- The Computational Auditory Signal Processing and Perception Model (CASP): A Revised Version 97%
- A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The inner ear's active process contributes to selective attention to speech in noise 94%
- Neural correlates of masked and unmasked tones: psychoacoustics and late auditory evoked potentials (LAEPs) 93%
- Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.