A uniform oversampled filter bank approach to independent component analysis

We present a new approach to perform independent component analysis (ICA) for convolved mixtures. This approach is based on filter banks, and a simplified network efficiently performs ICA with decimated signals in each subband. Decimation provides much less computational complexity and faster convergence speed than the time domain approach. Furthermore, the approach does not have a performance limitation of the frequency domain approach, and it is able to select the number of filters in the filter bank regardless of reverberation. With an oversampled filter bank, adaptive parameters can be adjusted without any information of other subbands, and the approach is suitable for parallel processing. We verify the effectiveness of the filter bank approach through simulations on adaptive noise cancelling.