REDUCTION OF ENERGY FOR IOT BASED SPEECH SENSORS IN NOISE REDUCTION USING MACHINE LEARNING MODEL.

Human communication is mostly used in a variety of ways, including human-machine communication, technical equipment, and even virtual support or search engines. This kind of communication is often carried out using a device that is sensitive to background noise. It has a detrimental impact on the message’s or content’s comprehension, and it also lowers communication quality. The quality of the voice signal could be highly reduced as it travels from the transmission to reception. Improved nonlinear filter analysis has been attempted to solve step size difficulties as well. However, each approach has its drawbacks. As a result, an effective method is required to overcome all current disadvantages. The objective of the proposed work is to detect speech degradations in both sustained vowels and speech. This research recommends utilizing a Variable Step Size Normalized Differential Least Mean Square (VSSNDLMS) algorithm to detect speech degradations in both sustained vowels and speech. The value of the alpha parameter is changed to reduce background noise in speech communications. The suggested system’s performance is evaluated and contrasted with that of the currently used methods. Using the suggested technology, noise has been reduced in the voice signals. [ABSTRACT FROM AUTHOR]