Wind Noise Reduction in Single Channel Speech Signals |
Kristian Timm Andersen
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| Abstract | 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 speci cally 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 Matrix Factorization. |
| Keywords | Non-negatiev matrix factorization, single channel speech separation |
| Type | Master's thesis [Academic thesis] |
| Year | 2008 Month February |
| Publisher | Department of Informatics and Mathematical Modelling |
| Address | Richard Petersens Plads, B321, DK-2800 Kongens Lyngby |
| IMM no. | IMM-M.Sc.-2008-18 |
| Note | Supervisor: Jan Larsen and Mikkel N. Schimdt |
| Electronic version(s) | [pdf] |
| Publication link | http://orbit.dtu.dk/Publications,resultSetTable.recordLink.sdirect?sp=211453 |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |