Work

Recognition of Noises and Noise Speech Signals by Artificial Neural Networks

Year: 2022

Type: conference-paper

Abstract: According to the studies, the most commonly used mathematical apparatuses for signal recognition tasks are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs). This paper presents an innovative approach related to the possibility for identification of accidental noise impacts and human speech with superimposed presence of noise by Backpropagation Neural Networks (BNNs) in different transfer functions. BPNs with linear, tangent-sigmoid and log-sigmoid transfer functions in the output layers are tested. A neural architecture for noise recognition in 6 neurons defined in the hidden layer with “tansig” activation output and achieved accuracy of 98.3% is selected. In the speech processing, an identical efficiency of 93.7% in 4, 3 and 4 hidden neurons for all types of output transfer functions was observed.

Source: International Conference on Biomedical Innovations and Applications (BIA)

DOI: 10.1109/bia52594.2022.9831313

Authors: Ivelina Balabanova, Stela Kostadinova, Georgi Georgiev

Institutions: Technical University of Gabrovo, Technical University of Varna

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4286286237

FWCI: 0.105

Cites: 3

Cited by: 1

Related to: 10