The use of virtual keyboards in mobile devices such as smartphones and tablets has become an essential tool for inputting information. The sound of keystrokes has been observed in previous studies to be recorded along with ambient noises, such as those produced by uncontrolled student noise, fans, doors and windows, moving cars, and similar sources. The presence of such noises negatively affects the quality of the keystrokes signal, which in turn affects keystroke analysis. The traditional FFT-based denoising methods are vital but they are often limited by their inability to adapt to the varying characteristics of real-world audio and noises. This paper proposes an enhanced Fast Fourier Transform (FFT) with an adaptive threshold technique that reduces ambient noises. The adaptive threshold technique is developed to identify frequency bins that contain noise and set their sizes to zero or attenuate them to reduce the noise. The paper evaluates the performance of the enhanced FFT with adaptive threshold on keystrokes recorded audio and validates it through extensive experimentation. The results show that the enhanced FFT outperforms the traditional FFT in terms of speed and the amount of noise removed from the recorded audio signal, indicating a significant improvement.