Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source

•Reverberation is a phenomenon unavoidable in enclosures, resulting in reduced intelligibility for hearing impaired and non-native listeners and even for the normal hearing listeners in noisy circumstances.•It also results in degraded performance of the machine listening applications.•Our proposed model has outperformed the classical signal processing dereverberation model ‘weighted prediction error’ in terms of cepstral distance (CEP), frequency weighted segmental signal to noise ratio (fwsegSNR) and signal-to-reverberation modulation energy ratio (SRMR) by 1.4 points, 8 dB and 0.6 dB. It has achieved better performance than the deep learning based dereverberation model by gaining 1.3 points improvement in CEP with comparable fwsegSNR, using training dataset which is almost 8 times smaller than required for that model.•The proposed model also sustained its performance under relatively similar unseen acoustic conditions and at positions in the vicinity of its training position.