Cochlear implants based on neurobiology and convolutional neural network have been created

Speech and voice recognition technology has made great strides over the past few decades. But

They all have one thing in common:Despite what they look like, none of them work in real time. Each is based on hardware and software that processes what is heard. In a new study, scientists suggest that the problem with current devices is the complexity of the calculations that need to be performed. To solve this, they created a model that simulates hearing in humans, which is based on combining the best features of convolutional neural networks with computational neuroscience.

Convolutional neural network - ad hocArtificial neural network architecture, proposed by Jan Lekun in 1988 and aimed at efficient pattern recognition, is part of deep learning technologies.

Hearing in humans depends on different parts of the ear.Sound enters the ear canal and meets the eardrum. It vibrates in response, sending signals to the bones in the inner ear that create ripples in the fluid in the cochlea. This fluid mixes the hair cells that line the cochlea. The movement of hair cells stimulates ion channels, which in turn generate signals sent to the brain stem. Researchers in Belgium have created an artificial intelligence (AI) system that has been taught to recognize sound and then decode it in the same way. They then hooked up their system to a model based on human anatomy. They called their system CoNNear, a working snail model.

Testing has shown that the system is capable ofConvert 20 kHz sampled acoustic waves into cochlear basilar membrane waveforms in real time, far ahead of modern conventional systems. CoNNear functions as a cochlea 2000 times faster than modern hearing aids. The researchers speculate that their discoveries will lay the foundation for a new generation of human hearing aids or devices with enhanced hearing and speech recognition.

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