Search Results for author: Arshdeep Singh

Found 9 papers, 3 papers with code

Efficient Similarity-based Passive Filter Pruning for Compressing CNNs

no code implementations27 Oct 2022 Arshdeep Singh, Mark D. Plumbley

However, the computational complexity of computing the pairwise similarity matrix is high, particularly when a convolutional layer has many filters.

Acoustic Scene Classification Scene Classification

Low-complexity CNNs for Acoustic Scene Classification

no code implementations2 Aug 2022 Arshdeep Singh, James A King, Xubo Liu, Wenwu Wang, Mark D. Plumbley

This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC).

Acoustic Scene Classification Classification +1

Continual Learning For On-Device Environmental Sound Classification

1 code implementation15 Jul 2022 Yang Xiao, Xubo Liu, James King, Arshdeep Singh, Eng Siong Chng, Mark D. Plumbley, Wenwu Wang

Experimental results on the DCASE 2019 Task 1 and ESC-50 dataset show that our proposed method outperforms baseline continual learning methods on classification accuracy and computational efficiency, indicating our method can efficiently and incrementally learn new classes without the catastrophic forgetting problem for on-device environmental sound classification.

Classification Continual Learning +1

1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality

no code implementations31 Mar 2022 Arshdeep Singh

This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC).

Acoustic Scene Classification Dictionary Learning +2

A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification

1 code implementation29 Mar 2022 Arshdeep Singh, Mark D. Plumbley

We propose a passive filter pruning framework, where a few convolutional filters from the CNNs are eliminated to yield compressed CNNs.

Acoustic Scene Classification Scene Classification

Health Monitoring of Industrial machines using Scene-Aware Threshold Selection

no code implementations21 Nov 2021 Arshdeep Singh, Raju Arvind, Padmanabhan Rajan

In classification, our hypothesis is that the reconstruction error computed for an abnormal machine is larger than that of the a normal machine, since only normal machine sounds are being used to train the autoencoder.

Scene Classification

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