Aidan Hogg obtained an MEng degree in Electronic and Information Engineering (First-Class Honours) from Imperial College London in 2017.
He is now a PhD student graduating from Imperial College London in 2021 with developed professional interests in Speech & Audio Signal Processing. He has over 2 years working in various engineering roles including both software and hardware development.
His research is in speaker diarisation which aims at answering the question ‘which speaker spoke when?’. More formally this requires the unsupervised identification of each speaker within an audio stream and the intervals during which each speaker is active.
et al., Non-Intrusive POLQA estimation of speech quality using recurrent neural networks, European Signal Processing Conference (EUSIPCO), IEEE
Hogg A, Evers C, Naylor P, Multiple hypothesis tracking for overlapping speaker segmentation, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE
Hogg A, Naylor P, Evers C, Speaker change detection using fundamental frequency with application to multi-talker segmentation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE