Matlab Simulation Using Kalman Filter Algorithm to Reduce Noise in Voice Signals

Abstract

Sound signals polluted by noise are a common problem in various audio applications, including communication, sound processing, and audio recording. In this article, proposes the use of Kalman Filter algorithm as an effective method to reduce noise in speech signals. Simulations are performed using Matlab software to implement the Kalman Filter algorithm on noise polluted voice signals. The study includes several important steps, including the input of noise-polluted speech signals and the implementation of the Kalman Filter to clean the signals. Simulation results are measured using commonly used audio quality metrics, such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE), to evaluate the effectiveness of the algorithm. The results from the simulations show that the use of the Kalman Filter algorithm significantly improves the quality of noise-contaminated speech signals. These results indicate that this algorithm can be a potential solution to the problem of noise reduction in audio applications. In addition, the implementation in the Matlab environment allows for easy testing and adaptation of this algorithm for different types of audio applications. This research makes a positive contribution to the development of more efficient noise reduction techniques in speech signal processing, focusing on the use of the Kalman Filter algorithm and its implementation using Matlab software. The implications of this research can be potentially beneficial in improving the quality of sound signals in various audio application contexts.