ArticleNature machine intelligence2021
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 60 citations in OpenAlex.
- A DEEP NEURAL NETWORK FOR PREDICTING CONTINUOUS HUMAN EEG ACROSS THE AUDITORY PATHWAY IN RESPONSE TO SOUND.ArXiv · 2026Article
- Scalable photonic reservoir computing for parallel machine learning tasks.Nature communications · 2025Article
- Animal Species Classification from Vocalizations Using Cochlear-Inspired Audio Features and Machine Learning.Biomimetics (Basel, Switzerland) · 2025Article
- Piezoelectric nanofiber-based intelligent hearing system.Science advances · 2025Article
- Modelling neural coding in the auditory midbrain with high resolution and accuracy.Nature machine intelligence · 2025Article
- Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing.Nature communications · 2024Article
- Models optimized for real-world tasks reveal the task-dependent necessity of precise temporal coding in hearing.bioRxiv : the preprint server for biology · 2024Article
- Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss.eLife · 2023Article
- An outer hair cell-powered global hydromechanical mechanism for cochlear amplification.Hearing research · 2022Article
- Deep neural network models of sound localization reveal how perception is adapted to real-world environments.Nature human behaviour · 2022Article
- Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility.Frontiers in neuroscience · 2022Article
- A comparative study of eight human auditory models of monaural processing.Acta acustica. European Acoustics Association · 2022Article
- A convolutional neural-network framework for modelling auditory sensory cells and synapses.Communications biology · 2021Article
- Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Perception.Frontiers in neuroscience · 2020Article
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 at 1 institution in 1 country.
Funding
Abstract
Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications.
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.