Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Directional, shift-insensitive, complex wavelet transforms with controllable redundancy
Directional, shift-insensitive, complex wavelet transforms with controllable redundancy
A new framework for complex wavelet transforms
IEEE Transactions on Signal Processing
Codebook-Based Bayesian Speech Enhancement for Nonstationary Environments
IEEE Transactions on Audio, Speech, and Language Processing
Codebook driven short-term predictor parameter estimation for speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Audio source separation with a single sensor
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
We address the problem of blind source separation from a single channel audio source using a statistical model of the sources. We modify the Bark Scale aligned Wavelet Packet Decomposition, to acquire approximate-shiftability property. We allow oversampling in some decomposition nodes to equalize sampling rate in all terminal nodes. Statistical models are trained from samples of each source separately. The separation is performed using these models. The proposed psycho-acoustically motivated non-uniform filterbank structure reduces signal space dimension and simplifies training procedure of the statistical model. In our experiments we show that the proposed algorithm performs better when compared to a competing algorithm. We study the effect that different wavelet families have on the performance of the proposed signal analysis in the single-channel source separation task.