Audio Noise Reduction Using Spectral Subtraction

Abstract

Spectral subtraction is used in this research as a method to remove noise from noisy speech signals by operating in the frequency domain. This method leverages the distinct spectral characteristics of noise and the desired audio signal to attenuate unwanted noise while preserving the integrity of the original content and consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using MATLAB software by 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). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth of overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech. The process begins with noise estimation, typically derived from segments of the signal where only noise is present. This noise profile is subtracted from the noisy signal's magnitude spectrum during the frequency analysis, followed by reconstruction of the audio signal through inverse transformation. Despite its effectiveness, spectral subtraction can introduce artifacts, such as residual "musical" noise, especially in non-stationary noise environments. Various  enhancements,    including  adaptive techniques and multi-band spectral subtraction, have been developed to address these challenges. Spectral subtraction is widely applied in fields such as telecommunications,     hearing     aids,    and    audio restoration, demonstrating its importance in improving audio quality in noisy environments.