A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
This work addresses the need for real-time, human-like auditory models for applications such as machine hearing and hearing aids, representing an incremental improvement by combining existing methods.
The authors tackled the problem of computationally expensive biophysical auditory models by developing a hybrid convolutional neural network model (CoNNear) that accurately simulates human cochlear mechanics and filter tuning in real-time, achieving accurate simulation of frequency selectivity and intensity dependence essential for robust speech intelligibility.
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.