ArticlePNAS nexus2026
GSP Cochlea: A graph signal processing approach for studying sound encoding.
Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Humans are able to hear in a variety of complicated acoustic environments. This feat begins in the peripheral auditory system, where the cochlea collects and transmits thousands of individual bits of sound data to the brain. Here, we introduce GSP Cochlea: a graph signal processing-based framework to investigate and visualize sound encoding. We show that a cochlea graph with a mesh topology provides a mechanism of denoising, efficient information transfer, and modular processing. We demonstrate an application to assess hearing loss as more than just a decibel loss at particular frequencies of sound but also a significant change to the cochlea graph architecture. GSP Cochlea is a generalized approach that provides new insight into the higher-level functional activity of the inner ear.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.