Wind Noise Reduction in Single Channel Speech Signals
Kristian Timm Andersen · 2008
In this thesis a number of wind noise reduction techniques have been reviewed, implemented and evaluated. The focus is on reducing wind noise from speech in single channel signals. More specifically a generalized version of a Spectral Subtraction method is implemented along with a Non-Stationary version that can estimate the noise even while speech is present. Also a Non-Negative Matrix Factorization method is implemented. The PESQ measure, different variations of the SNR and Noise Residual measure, and a subjective MUSHRA test is used to evaluate the performance of the methods. The overall conclusion is that the Non-Negative Matrix Factorization algorithm provides the best noise reduction of the investigated methods. This is based on both the perceptual and energy-based evaluation. An advantage of this method is that it does not need a Voice Activity Detector (VAD) and only assumes a-priori information about the wind noise. In fact, the method can be viewed solely as an advanced noise estimator. The downside of the algorithm is that it has a relatively high computational complexity. The Generalized Spectral Subtraction method is shown to improve the speech quality, when used together with the Non-Negative Matric Factorization.