Comparison of spectral subtraction noise reduction algorithms

Noise in media is any undesirable signal that masks relevant information content. The addition of noise to real-world data in any context is practically inevitable. Noise reduction algorithms in the past have addressed the problem but lacked adaptability to various real-world applications while also being time and resource extensive. Spectral subtraction provides a hybrid approach to noise reduction that incorporates versatility and efficient resource usage. This research tested the performance of two spectral subtraction noise reduction algorithms (stationary and non-stationary) across five categories of real-world noise (speech only, speech with natural noise, music, animal sounds, and noise only). The research question under study was how stationary and non-stationary spectral subtraction algorithms differ in their noise reduction performance when subjected to the various categories of noise. The testing was done based on normalized cross-correlation, which is the similarity between the noise-reduced audio and the original recording in each case. Non-stationary spectral subtraction performed better in samples where human speech was the target: speech only and speech with natural noise. Stationary spectral subtraction performed better when denoising music and animal sounds. This anomaly in performance between the two algorithms was only noted in categories with no human speech. These results exemplify the performance and versatility of different spectral subtraction algorithms. The category-specific results can be used to employ specific spectral subtraction algorithms for specific tasks for optimum performance.