To enhance noise compensation as well as the recovery of data, this paper enhances a better signal processing platform depending on the Fourier Transform, Wavelet Transform and Kalman Filtering. Similar to most other high-level signal processing applications, the analysis is concerned with the problem to do with interference and degradation through a two pronged method that employs frequency domain filtering as well as sequential noise reduction. We achieve nearly 40-percent SNR superiority on experiments to controls and approximately 30-percent smaller MSE with the Kalman Filter obtaining the maximum SNR with the varying SNR and noisy speech samples, respectively, which validates our assertion that the filter is actually a successful adaptive noise reducer. Also the AX system proposed ensured the control of the computational complexity as in the case of the Wavelet Transform capable of managing the localized noise of multiple resolutions. Such filtering algorithms and mathematical models are typical examples of this proposal of an adaptive and efficient process of maintaining the purity of data in erroneous situations.