HIGHLIGHTS
- who: Mahdi Barhoush from the INDA, RWTH Aachen, Aachen, Germany have published the research: Speaker identification and localization using shuffled MFCC features and deep learning, in the Journal: (JOURNAL)
- what: The authors propose a new end-to-end identification and localization model based on a simple fully connected deep neural network (FC-DNN) and just two input microphones. In this regard the authors propose using a novel Mel Frequency Cepstral Coefficients (MFCC) based feature called Shuffled MFCC (SHMFCC) and its variant Difference Shuffled MFCC (DSHMFCC). To test the approach the authors analyzed the performance of . . .
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