Multi-channel time-frequency data fusion

This paper proposes an efficient mechanism for the fusion of two noisy speech signals obtained by an array of two microphones using single-tap time-frequency filters and by taking into account the correct time delay of arrival (TDOA) of the speech source. Speech signals obtained by the microphones are transformed into a set of two complex time-frequency (TF) images. By knowing the correct TDOA, and therefore the associated phase difference between the signals at each frequency, it is possible to non-linearly filter both the real and the imaginary parts of the TF images. This will consist of a TF reward-punish filter that adjusts the amplitude of the TF blocks based upon the variation of their phase-difference with the ideal phase-difference defined by the TDOA. Simulation results show that the proposed technique can achieve a Signal-to-Noise Ratio (SNR) improvement of 15 dB when there, is strong Gaussian noise present (-20 dB initial SNR). When the original SNR is OdB, the simulated improvement is approximately 8 dB. It is also shown that although the proposed technique is a more general case of the adaptive beamformer (where the adaptive beamformer has a specific reward-punish characteristic), other reward-punish characteristics that are proposed in this paper can often surpass the performance of the ideal adaptive beamformer.