ArticleScientific reports2025
A multi-stage auditory model for binaural sound localization using the locally competitive algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Localization Algorithms for Hearing Devices Influenced by Individual Variability in Ear Acoustics.Biomimetics (Basel, Switzerland) · 2026Article
- Dynamic sensor adaptation based on efferent feedback for adaptive bio-inspired sound source localization.Frontiers in neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human auditory system's ability to accurately localize sounds is essential for navigating and interpreting our environment. However, modern hearing aids and cochlear implants often disrupt critical binaural hearing cues, posing challenges for individuals with hearing impairments. Additionally, precise sound localization is vital for technologies such as smart-home devices, autonomous vehicles, and robotics. In this work we introduce a brain-inspired neural network model, utilizing sparse coding techniques, that can achieve high accuracy in azimuthal sound localization by leveraging binaural and spectral auditory cues. The proposed model achieves a localization accuracy of 95%, comparable to human performance, through a novel application of the Locally Competitive Algorithm (LCA) for efficient sparse coding of auditory signals. This approach not only enhances our understanding of neural auditory processing but also holds promise for improving hearing aids and other auditory-based technologies, thereby advancing individual well-being and technological interactions within complex auditory environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.