Low-complexity noise power spectral density estimation for harsh automobile environments

In this paper a simple yet robust noise power spectral density estimation algorithm is presented. The motivation for the algorithm developed here is the harsh noise environment present in automobiles along with the need to keep the complexity low for real-time implementations. The scheme is based on a multiplicative estimator in which multiple increment and decrement time-constants are utilized. The time-constants are chosen based on noise-only and speech-like situations. Further, by observing the long-term “trend” of the noisy input spectrum, suitable time-constants are chosen which reduces the tracking delay significantly. The trend factor is measured taking into account the dynamics of speech. Evaluation of the proposed algorithm and comparisons with state of the art systems will be performed in the context of varying automobile noises.