2023-24

• A Choudhary, A Singh, V Abrol, M D. Plumbley, “Efficient CNNs with Quaternion Transformations and Pruning for Audio Tagging”, Interspeech 2024 (Accepted).

• A Singh, T Deacon, M D. Plumbley, "Environmental sound classification using raw-audio based ensemble framework", accepted in Internoise 2024.

• T Deacon, A Singh, G Bibbo and M D. Plumbley, "Soundscape Personalisation at Work: Designing AI-Enabled Sound Technologies for the Workplace", Sound and Music Computing Conference  (SMC) 2024.

• E Corrigan-Kavanagh, A Singh, D Frohlich, M D. Plumbley, "Designing AI for Home Wellbeing and Implications for Future Healthcare Technologies", accepted in Design4Health conference, Sheffield Hallam University.

• Gabriel Bibbo, A Singh, Mark D. Plumbley, "Audio Tagging on an Embedded Hardware Platform", accepted in Internoise, 2024.

2021-23:

• JA Kingh, A Singh, Mark D Plumbley, "Compressing audio CNNs with graph centrality based filter pruning"  accepted in IEEE WASPAA 2023 (oral ppt)

• [Hardware demo] G Bibbo, A Singh, Mark D Plumbley, "Recognise and Notify Sound Events using a Raspberry PI based Standalone Device ", accepted as a demo paper for IEEE WASPAA 2023, Demo video link

• Arshdeep Singh , H Liu, and Mark D. Plumbley. "E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks." Internoise 2023. Oral ppt link

• Arshdeep Singh and Mark D. Plumbley, "Efficient CNNs via passive filter pruning", (Under peer-review)

• Arshdeep Singh and Mark D. Plumbley, "A case study on Efficient Audio-based CNNs via Filter Pruning", Open research case studies, University of Surrey, UK.

• Arshdeep Singh and Mark D. Plumbley,. "Efficient Similarity-based Passive Filter Pruning for Compressing CNNs." accepted in ICASSP 2023.

• Arshdeep Singh and Mark D. Plumbley, "Low-complexity CNNs for acoustic scene classification." DCASE 2022 workshop, Nancy, France.

• Xiao, Y., Liu, X., King, J., Singh, A., Chng, E. S., Plumbley, M. D., & Wang, W. (2022). Continual Learning For On-Device Environmental Sound Classification, DCASE 2022 workshop.

• Arshdeep Singh, James A Kingh, Xubo Liu, Wenwu Wang and Mark D. Plumbley, " Low-Complexity CNNs for Acoustic Scene Classification", Techincal Report, DCASE 2022 challenge.

• Arshdeep Singh, Mark D. Plumbley, " A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification", Interspeech 2022.

• Arshdee Singh, Mark D. Plumbley, "Reducing computational complexity of convolutional neural networks through filter pruning",  Poster presentation at  "Exploring New Possibilities" 2022 Doctoral College Conference at University of Surrey, UK.

• Arshdeep Singh, Raju Arvind, Padmanabhan Rajan, "Health monitoring of industrial machines using Scene-aware threshold selection", Oral presentation at  "Exploring New Possibilities" 2022 Doctoral College Conference at University of Surrey, UK.

• Arshdeep Singh, Mark D. Plumbley, "Efficient Audio-based CNNs via Filter Pruning", at Surrey Annual Open Research Lecture: Open research poster session, April 2022.

2016-21:

• Arshdeep Singh, Dhanunjaya Varma Devalraju, Padmanabhan Rajan, "Pruning and Quantization for Low-Complexity Acoustic Scene Classification" DCASE 2021 Task 1A Challenge Technical Report.

• Arshdeep Singh, Dhanunjaya Varma Devalraju, Padmanabhan Rajan, "END2END CNN-BASED LOW-COMPLEXITY ACOUSTIC SCENE CLASSIFICATION", DCASE 2020 Challenge Technical Report.  

• Arshdeep Singh, Padmanabhan Rajan, Arnav Bhavsar, "SVD-Based Redundancy Removal in 1-D CNNs for Acoustic Scene Classification", Pattern Recognition Letters, Volume 131, March 2020, Pages 383-389 ,  https://authors.elsevier.com/a/1aXlDcAmyjI6C 

• Arshdeep Singh, Padmanabhan Rajan, Arnav Bhavsar, "Deep Multi-View Features from Raw Audio for Acoustic Scene Classification", DCASE 2019 Workshop, New York, USA  ( https://doi.org/10.33682/05gk-pd08 ).

• Arshdeep Singh, Padmanabhan Rajan, Arnav Bhavsar “Deep Hidden Analysis: A Statistical Framework to Prune Feature Maps”  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019.

• Arshdeep Singh, Anshul Thakur, Padmanabhan Rajan, Arnav Bhavsar “A Layer-wise Score Level Ensemble Framework for Acoustic Scene Classification.” 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 2018.

• Arshdeep Singh, Anshul Thakur, Padmanabhan Rajan “APE: Archetypal-Prototypal Embeddings for Audio Classification.” IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, 2018.

• Anshul Thakur, Arshdeep Singh, Padmanabhan Rajan “Convex Likelihood Align- ments For Bioacoustic Classification.” IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark 2018.

• Aparna Akula, Arshdeep Singh, Ripul Ghosh, S. Kumar, H. K. Sardana, “Target Recognition in Infrared Imagery using Convolutional Neural Network”, in Proceedings of International Conference on Computer Vision and Image Processing, pp. 25-34. Springer, Singapore, 2017.

• Arshdeep Singh, Rajesh Mehra “Analysis of Field Variation on Threshold Voltage and Drain Current of NMOS Using TCAD”, proceedings of IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), ISBN No. 978-1-4799-3914- 5/14, 2014, pp 488-491.

• Arshdeep Singh, Deepika, Bhaskar Mishra, “Implementation of back-propagation neural network using SCILAB and its convergence speed improvement” proceedings of International conference EDIT, NITTTR Chandigarh ,Vol 2, issue 1, pp.192-194, 2015.