5 citations · 13 across the 5 of their papers we have counts for
5 papers
Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation
Mark Anderson, Tomi Kinnunen, Naomi Harte
While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic fronte…
Learnable Acoustic Frontends in Bird Activity Detection
Mark Anderson, Naomi Harte
Autonomous recording units and passive acoustic monitoring present minimally intrusive methods of collecting bioacoustics data. Combining this data with species agnostic bird activ…
Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals
Mark R. Anderson, Vasundhara Basu, Ryan D. Martin +4
We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we f…
Low Resource Species Agnostic Bird Activity Detection
Mark Anderson, John Kennedy, Naomi Harte
This paper explores low resource classifiers and features for the detection of bird activity, suitable for embedded Automatic Recording Units which are typically deployed for long…
Bioacoustic Event Detection with prototypical networks and data augmentation
Mark Anderson, Naomi Harte
This report presents deep learning and data augmentation techniques used by a system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021 Challenge. The remit wa…