8 citations · 23 across the 12 of their papers we have counts for
12 papers
Environmental Sound Classification on An Embedded Hardware Platform
Gabriel Bibbo, Arshdeep Singh, Mark D. Plumbley
Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on r…
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…
Efficient CNNs via Passive Filter Pruning
Arshdeep Singh, Mark D. Plumbley
Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computation…
Efficient Similarity-based Passive Filter Pruning for Compressing CNNs
Arshdeep Singh, Mark D. Plumbley
Convolution neural networks (CNNs) have shown great success in various applications. However, the computational complexity and memory storage of CNNs is a bottleneck for their depl…
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…
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality
Arshdeep Singh
This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC). A raw audio signal is represented…
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification
Arshdeep Singh, Mark D. Plumbley
We present a method to develop low-complexity convolutional neural networks (CNNs) for acoustic scene classification (ASC). The large size and high computational complexity of typi…
Health Monitoring of Industrial machines using Scene-Aware Threshold Selection
Arshdeep Singh, Raju Arvind, Padmanabhan Rajan
This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained…
Finding High-Value Training Data Subset through Differentiable Convex Programming
Soumi Das, Arshdeep Singh, Saptarshi Chatterjee +2
Finding valuable training data points for deep neural networks has been a core research challenge with many applications. In recent years, various techniques for calculating the "v…
Embedded CNN based vehicle classification and counting in non-laned road traffic
Mayank Singh Chauhan, Arshdeep Singh, Mansi Khemka +2
Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain. However, the wide variety of vehicles (two-wheelers, three-whe…