Suppression of acoustic noise in speech using spectral subtraction

TLDR

Digital speech processors in real‑world settings need noise suppression, and spectral subtraction offers a computationally efficient, processor‑independent approach. The paper presents a stand‑alone algorithm to reduce acoustically added noise in speech. The algorithm suppresses stationary noise by subtracting a spectral bias estimated during silence, then applies secondary steps to reduce residual noise, and resynthesizes the speech waveform for use as a pre‑processor in narrow‑band voice, recognition, or authentication systems.

Abstract

A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.