Exploring the feasibility of a two-layer NN-based sound classifier for hearing aids

This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance the listening comprehension when the user goes from a sound environment to another different one. The approach consists in dividing the classifying algorithm into two layers that make use of two neural network algorithms that work more efficiently: the input signal discriminated by the first layer into either speech or non-speech is ulteriorly classified more specifically depending on whether the user is in a conversation (both in quiet and in the presence of background noise) or in a noisy ambient in the absent of speech. The system results in having three classes, labeled “speech in quiet”, “speech in noise”, and “noise”. A brief discussion on the computational complexity of this approach illustrates its feasibility to be implemented on a conventional digital hearing aid.