5 citations · 11 across the 5 of their papers we have counts for
5 papers
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…
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,…
Low-complexity CNNs for Acoustic Scene Classification
Arshdeep Singh, James A King, Xubo Liu +2
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC…
Low-complexity CNNs for Acoustic Scene Classification
Arshdeep Singh, Mark D. Plumbley
This paper presents a low-complexity framework for acoustic scene classification (ASC). Most of the frameworks designed for ASC use convolutional neural networks (CNNs) due to thei…
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…