Biblio

Physical-layer key (PLK) generation scheme is a new key generation scheme based on wireless channel reciprocity. However, the security of physical layer keys still lacks sufficient theoretical support in the presence of eavesdropping attacks until now, which affects the promotion in practical applications. By analyzing the propagation mode of multipath signals under non-line-of-sight (nLoS), an improved spatial cross-correlation model is constructed, where the spatial cross-correlation is between eavesdropping channel and legitimate channel. Results show that compared with the multipath and obstacle distribution of the channel, the azimuth and distance between the eavesdropper and the eavesdropped user have a greater impact on the cross-correlation.

The speech emotion recognition accuracy of prosody feature and voice quality feature declines with the decrease of SNR (Signal to Noise Ratio) of speech signals. In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for robust speech emotion recognition. The W-WPCC feature is computed by combining the sub-band energies with sub-band spectral centroids via a weighting scheme to generate noise-robust acoustic features. And Deep Belief Networks (DBNs) are artificial neural networks having more than one hidden layer, which are first pre-trained layer by layer and then fine-tuned using back propagation algorithm. The well-trained deep neural networks are capable of modeling complex and non-linear features of input training data and can better predict the probability distribution over classification labels. We extracted prosody feature, voice quality features and wavelet packet cepstral coefficients (WPCC) from the speech signals to combine with W-WPCC and fused them by Deep Belief Networks (DBNs). Experimental results on Berlin emotional speech database show that the proposed fused feature with W-WPCC is more suitable in speech emotion recognition under noisy conditions than other acoustics features and proposed DBNs feature learning structure combined with W-WPCC improve emotion recognition performance over the conventional emotion recognition method.