Sound Classification using Deep Learning for Hard of Hearing and Deaf People
Md. Adnan Habib, Zarif Raiyan Arefeen, Arafat Hussain, S.M. Rownak Shahriyer, Tanzid Islam, Rafeed Rahman, Mohammad Zavid Parvez · 2022 International Conference on Inventive Computation Technologies (ICICT) · 2022
The proposed paper mainly focuses on developing an audio classification for people, who cannot hear properly, using convolutional neural network and recurrent neural network. One of the main problems that a hearing aid user faces is excessive background noise. Hearing aids with background noise classification algorithms can modify the response based on the noisy environment. Speech, azan, and ambient noises are all examples of significant audio signals. Whenever a human hears a sound, they can easily identify the sound, however it’s not the same for computers, and the algorithm must be fed with datasets in order to make it distinguish between different sounds. Hence, a system for people who have problems to hear has been built. A total of 98.67% and 97.01% accuracies after training and testing the data on convolutional neural network and recurrent neural network model respectively have been achieved.