DeepCNN:Spectro-temporal feature representation for speech emotion recognition

Speech emotion recognition(SER)is an important research problem in human-computer interaction systems.The representation and extraction of features are significant challenges in SER systems.Despite the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields.To mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based transformer.Two parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel CNNs.The learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer.This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map.The Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech emotions.With efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset respectively.The proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

Performance evaluation of multicast relay network using LDPC and convolutional channel codes along-with XOR network coding

In this paper, we have compared the performance of joint network channel coding （JNCC） for multicast relay network using low density parity check （LDPC） codes and Convolutional codes as channel codes while exclusive or （XOR） network coding used at the intermediate relay nodes. Multicast relay transmission is a type of transmission scheme in which two fixed relay nodes contribute in the second hop of end-to-end transmission between base transceiver station （BTS） and a pair of mobile stations. We have considered one way and two way multicast scenarios to evaluate the bit error rate （BER） and throughput performance. It has been shown that when using XOR network coding at the intermediate relay nodes, the same transmission becomes possible in less time slots hence throughput performance can be improved. Moreover we have also discussed two possible scenarios in the proposed system model, in which both diversity and multiplexing gain has been considered. It is worth notifying that BER and throughput achieved for LDPC codes is better than Convolutional codes for all the schemes discussed.