414 citations · 763 across the 20 of their papers we have counts for
9 papers · 1 filter
E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks
Arshdeep Singh, Haohe Liu, Mark D. Plumbley
Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learni…
Universal Source Separation with Weakly Labelled Data
Qiuqiang Kong, Ke Chen, Haohe Liu +4
Universal source separation (USS) is a fundamental research task for computational auditory scene analysis, which aims to separate mono recordings into individual source tracks. Th…
Compressing audio CNNs with graph centrality based filter pruning
James A King, Arshdeep Singh, Mark D. Plumbley
Convolutional neural networks (CNNs) are commonplace in high-performing solutions to many real-world problems, such as audio classification. CNNs have many parameters and filters,…
Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learning
Haohe Liu, Xubo Liu, Xinhao Mei +3
Few-shot audio event detection is a task that detects the occurrence time of a novel sound class given a few examples. In this work, we propose a system based on segment-level metr…
Continual Learning For On-Device Environmental Sound Classification
Yang Xiao, Xubo Liu, James King +4
Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation re…
Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks
Emad M. Grais, Gerard Roma, Andrew J. R. Simpson +1
The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To…