Filterbanks are often used as preprocessing stages in many applications and can be used for e.g. feature extraction or dimensionality reduction. In this paper, an unsupervised learning algorithm is described that develops a bank of parameterized (Gabor) filters, starting from a training set. It forces the filters to partition the input space in an equitable manner: each filter is tuned to a different frequency region and contributes equally to the extraction of localized features. The algorithm is tested on a real world example, after which the results are discussed.