Areas of specialism

Software Engineering; Signal Processing; Deep Learning and Optimization

My publications


The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous signals under the condition that only non-anomalous (normal) data is available beforehand. In UAD under DomainShift Conditions (UAD-S), data is further exposed to contextual changes that are usually unknown beforehand. Motivated by the difficulties encountered in the UAD-S task presented at the 2021 edition of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, we visually inspect Uniform Manifold Approximations and Projections (UMAPs) for log-STFT, logmel and pretrained Look, Listen and Learn (L3) representations of the DCASE UAD-S dataset. In our exploratory investigation, we look for two qualities, Separability (SEP) and Discriminative Support (DSUP), and formulate several hypotheses that could facilitate diagnosis and developement of further representation and detection approaches. Particularly, we hypothesize that input length and pretraining may regulate a relevant tradeoff between SEP and DSUP. Our code as well as the resulting UMAPs and plots are publicly available.

EMILY MARY CORRIGAN-KAVANAGH, MARK DAVID PLUMBLEY, MARC GREEN, ANDRES FERNANDEZ (2021)Exploring Sound Sensing to Improve Quality of Life in Urban Living

Following the successful application of AI and machine learning technologies to the recognition of speech and images, computer systems can now automatically analyse and recognise everyday real-world sound scenes and events. This new technology presents promising potential applications in environmental sensing and urban living. Specifically, urban soundscape analysis could be used to monitor and improve soundscapes experienced for people in towns and cities, helping to identify and formulate strategies for enhancing quality of life through future urban planning and development.