Learning Auditory Neural Representations for Emotion Recognition

Auditory emotion recognition has become a very important topic in recent years. However, still after the development of some architectures and frameworks, generalization is a big problem. Our model examines the capability of deep neural networks to learn specific features for different kinds of auditory emotion recognition: speech and music-based recognition. We propose the use of a cross-channel architecture to improve the generalization aspects of complex auditory recognition by the integration of previously learned knowledge of specific representation into a high-level auditory descriptor. We evaluate our models using the SAVEE dataset, the GTZAN dataset and the EmotiW corpus, and show comparable results with state-of-the-art approaches.

@InProceedings{BWW16, 
 	 author =  {Barros, Pablo and Weber, Cornelius and Wermter, Stefan},  
 	 title = {Learning Auditory Neural Representations for Emotion Recognition}, 
 	 booktitle = {International Joint Conference on Neural Networks (IJCNN)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {921--928},
 	 year = {2016},
 	 month = {Jul},
 	 publisher = {},
 	 doi = {10.1109/IJCNN.2016.7727297}, 
 }