We describe denoising one-dimensional signals by thresholding
Blackman windowed Gabor transforms. This method is
compared with Gauss-windowed Gabor threshold denoising and
wavelet-based denoising, and is found to be superior
in most cases.  A new, localized estimator of noise
standard deviation is also obtained.  Our work provides
the first step in developing an adaptive denoising method for
non-stationary noise.