Search Results for author: Arshdeep Singh

Found 14 papers, 6 papers with code

Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

no code implementations30 Oct 2023 Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, Abhinav Sethy

To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.

Language Modelling Retrieval

Audio Tagging on an Embedded Hardware Platform

1 code implementation15 Jun 2023 Gabriel Bibbo, Arshdeep Singh, Mark D. Plumbley

In this paper, we analyze how the performance of large-scale pretrained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as Raspberry Pi.

Audio Classification Audio Tagging

E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks

1 code implementation30 May 2023 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.

Audio Tagging

Compressing audio CNNs with graph centrality based filter pruning

no code implementations5 May 2023 James A King, Arshdeep Singh, Mark D. Plumbley

For large-scale CNNs such as PANNs designed for audio tagging, our method reduces 24\% computations per inference with 41\% fewer parameters at a slight improvement in performance.

Acoustic Scene Classification Audio Classification +2

Efficient CNNs via Passive Filter Pruning

no code implementations5 Apr 2023 Arshdeep Singh, Mark D. Plumbley

In comparison to the existing active filter pruning methods, the proposed pruning method is at least 4. 5 times faster in computing filter importance and is able to achieve similar performance compared to that of the active filter pruning methods.

Computational Efficiency Image Classification +1

Efficient Similarity-based Passive Filter Pruning for Compressing CNNs

1 code implementation27 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 Computational Efficiency +3

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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