Increasing Additive Noise Removal in Speech Processing Using Spectral Subtraction

In this research, we present a technique to increase noise removal from noisy speech signals using spectral subtraction. The noise removal algorithm includes storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse fast Fourier transform (IFFT). Performance of the algorithm was evaluated by calculating the speech to noise ratio (SNR). The improvement technique involved varying the lengths of the Hanning time windows, as well as the degrees of data buffers overlapping. Further improvement was sought by using frames averaging technique, which consists in averaging various spectrum frames before removing the noise. Results showed that using one-fourth overlapped data buffers with 256 points Hanning windows and no frames averaging lead to the best performance in removing noise from the noisy speech.