Implementation of Fast Fourier Transform and Least Mean Square Algorithms in The Denoising Process of Audio Signal

Audio signals play an important role as a medium for storing information, such as lecture materials, interview results, and other archives. However, audio signals are often contaminated by noise, which is unwanted interference that can affect their quality. Therefore, a denoising process is needed to reduce or eliminate noise components in the signal. The Fast Fourier Transform (FFT) and Least Mean Square (LMS) algorithms are frequently used in the denoising process due to their simple and easy-to-implement steps. This research uses primary data, specifically audio signals recorded under two noise conditions: rain noise as Audio Signal 1 and guitar instrument noise as Audio Signal 2, both stored in WAV format. The denoising process was performed using MATLAB software and evaluated based on Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) metrics. Higher SNR values and lower MSE values indicate the success of the denoising process in improving audio signal quality. The results of this study demonstrate the effectiveness of the applied algorithms, where the SNR value reached 38.2596 dB with an MSE of 0.0000028211 for Audio Signal 1, and an SNR value of 38.6881 dB with an MSE of 0.0000014988 for Audio Signal 2. An SNR value between 25 dB and 40 dB is categorized as a very good signal, indicating that the quality of the processed audio signals falls into the very good signal category.