2025
Gabriel Bibbó, Arshdeep Singh, Thomas Deacon, Mark D. Plumbley, "Speech Removal Framework for Privacy-preserving Audio Recordings", Demo paper accepted at IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2025.(Demo link)
R Burchett-Vass, A Singh, G Bibbó, & Mark D Plumbley, Integrating IP Broadcasting with Audio Tags: Workflow and Challenges, Audio Engineering Society Conference on AI and Machine Learning for Audio (AES AIMLA), QMU, London (accepted) (link)
T Deacon, G bibbo, A Singh, and Mark D Plumbley, Soundscape experience mapping: A deep listening approach for eliciting older adults’ perceptions of indoor soundscapes. In Forum Acusticum Euronoise, 2025. (link)
A Singh, Haohe Liu ,Gabriel Bibbo,Thomas Deacon, Mark D. Plumbley, "Personalized live sound recognition using efficient PANNs", Show & Tell demo, ICASSP 2025. (link)
A Singh and M. D. Plumbley, "Efficient CNNs via Passive Filter Pruning," in IEEE Transactions on Audio, Speech and Language Processing, doi: 10.1109/TASLPRO.2025.3561589. (link)
2024
Bibbó, T Deacon, A Singh, Mark D. Plumbley, "The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection", 8th International Workshop on Speech Processing in Everyday Environments (CHiME 2024), Kos, Greece, pp.49-53, doi 10.21437/CHiME.2024-11, 2024. (download dataset) (Paper link)
X. Xu, A. Singh, M. Wu, W. Wang and M. D. Plumbley, "Investigating Passive Filter Pruning for Efficient CNN-Transformer Audio Captioning," IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), doi: 10.1109/MLSP58920.2024.10734745, 2024. (Paper link)
Chaudhary, A., Singh, A., Abrol, V., Plumbley, M.D., "Efficient CNNs with Quaternion Transformations and Pruning for Audio Tagging", Interspeech 2024, pp 1150-1154, doi: 10.21437/Interspeech.2024-1331, 2024. (Paper link)
A Singh, T Deacon, M D. Plumbley, "Environmental sound classification using raw-audio based ensemble framework", INTER-NOISE and NOISE-CON Congress and Conference Proceedings, INTER-NOISE24, Nantes, France, pp. 6402-6410, 2024. (Paper link)
T Deacon, A Singh, G Bibbo and M D. Plumbley, "Soundscape Personalisation at Work: Designing AI-Enabled Sound Technologies for the Workplace", Proceedings of the 21st Sound and Music Computing Conference, pp 116-126, DOI 10.5281/zenodo.13918961, 2024. (Paper link)
E Corrigan-Kavanagh, A Singh, D Frohlich, M D. Plumbley, "Designing AI for Home Wellbeing and Implications for Future Healthcare Technologies", Design4Health conference, Sheffield Hallam University. (Paper link)
Gabriel Bibbo, A Singh, Mark D. Plumbley, "Environmental sound classification on an embedded hardware platform", INTER-NOISE and NOISE-CON Congress and Conference Proceedings, INTER-NOISE24, Nantes, France, pp. 6376-6385, 2024. (Paper link)
2021-23:
J. A. King, A. Singh and M. D. Plumbley, "Compressing Audio CNNS with Graph Centrality Based Filter Pruning," 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, doi: 10.1109/WASPAA58266.2023.10248103, 2023. (Paper link)
Bibbó, Gabriel, A. Singh, and Mark D. Plumbley. "Recognise and Notify Sound Events Using a Raspberry PI Based Standalone Device [Demo]." IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2023). New Paltz, NY, USA. 2023. Demo video link
A Singh, Haohe Liu, and Mark D. Plumbley. "E-PANNs: sound recognition using efficient pre-trained audio neural networks." Inter-Noise and Noise-Con Congress and Conference Proceedings, Institute of Noise Control Engineering, vol 268 No 1, pp 7220-7228, 2023. (Paper link)
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.(Link)
A. Singh and M. D. Plumbley, "Efficient Similarity-Based Passive Filter Pruning for Compressing CNNs," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, doi: 10.1109/ICASSP49357.2023.10095560, 2023. (Paper link)
A. Singh & Mark D. Plumbley, "Low-complexity CNNs for acoustic scene classification." Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2022. (Link)
Xiao, Y., Liu, X., King, J., Singh, A., Chng, E. S., Plumbley, M. D., & Wang, W, "Continual Learning For On-Device Environmental Sound Classification", Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2022. (Link)
A Singh, James A King, Xubo Liu, Wenwu Wang and Mark D. Plumbley, " Low-Complexity CNNs for Acoustic Scene Classification", Techincal Report, DCASE 2022 challenge (Link).
A Singh, Mark D. Plumbley, " A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification", Interspeech 2022, pp. 2433-2437, doi: 10.21437/Interspeech.2022-10714, 2022.(Paper link)
A Singh, Mark D. Plumbley, "Reducing computational complexity of convolutional neural networks through filter pruning", Doctoral College Conference, University of Surrey, UK, https://doi.org/10.5281/zenodo.6613766, 2022.
A Singh, Raju Arvind, Padmanabhan Rajan, "Health monitoring of industrial machines using Scene-aware threshold selection", Doctoral College Conference, University of Surrey, UK, https://doi.org/10.5281/zenodo.6613783, 2022.
2016-21:
A Singh, D V Devalraju, P Rajan, "Pruning and Quantization for Low-Complexity Acoustic Scene Classification" DCASE 2021 Task 1A Challenge Technical Report. (Link)
A Singh, D V Devalraju, P Rajan, "END2END CNN-BASED LOW-COMPLEXITY ACOUSTIC SCENE CLASSIFICATION", DCASE 2020 Challenge Technical Report. (Link)
A Singh, P Rajan, A Bhavsar, "SVD-Based Redundancy Removal in 1-D CNNs for Acoustic Scene Classification", Pattern Recognition Letters, pp. 383-389, vol. 131, 2020.(Paper link)
A Singh, P Rajan, A Bhavsar, "Deep Multi-View Features from Raw Audio for Acoustic Scene Classification", Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), pp.229-233, https://doi.org/10.33682/05gk-pd08, 2019 (Paper link)
A. Singh, P. Rajan and A. Bhavsar, "Deep Hidden Analysis: A Statistical Framework to Prune Feature Maps," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 820-824, doi: 10.1109/ICASSP.2019.8682796, 2019. (Paper link)
A. Singh, A. Thakur, P. Rajan and A. Bhavsar, "A Layer-wise Score Level Ensemble Framework for Acoustic Scene Classification," 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, pp. 837-841, doi: 10.23919/EUSIPCO.2018.8553052, 2018. (Paper link)
A. Singh, A. Thakur and P. Rajan, "APE: Archetypal-Prototypal Embeddings for Audio Classification," IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, pp. 1-6, doi: 10.1109/MLSP.2018.8516945, 2018. (Paper link)
A. Thakur, A. Singh and P. Rajan, "Convex Likelihood Alignments for Bioacoustics Classification," IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, doi: 10.1109/MLSP.2018.8517063, 2018.(Paper link)
Aparna Akula, A Singh, Ripul Ghosh, S. Kumar, H. K. Sardana, “Target Recognition in Infrared Imagery using Convolutional Neural Network”, International Conference on Computer Vision and Image Processing, (pp. 25-34), volume 2 Springer Singapore, 2017. (Paper link)
A Singh, Rajesh Mehra “Analysis of Field Variation on Threshold Voltage and Drain Current of NMOS Using TCAD”, IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), ISBN No. 978-1-4799-3914- 5/14, pp 488-491, 2014. (Paper link)
A 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.