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Found 14 papers, 6 papers with code
Continual Learning For On-Device Environmental Sound Classification
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.
E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks
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 on an Embedded Hardware Platform
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.
Efficient Similarity-based Passive Filter Pruning for Compressing CNNs
However, the computational complexity of computing the pairwise similarity matrix is high, particularly when a convolutional layer has many filters.
Embedded CNN based vehicle classification and counting in non-laned road traffic
Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain.
Finding High-Value Training Data Subset through Differentiable Convex Programming
In this paper, we study the problem of selecting high-value subsets of training data.
Health Monitoring of Industrial machines using Scene-Aware Threshold Selection
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.
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification
We propose a passive filter pruning framework, where a few convolutional filters from the CNNs are eliminated to yield compressed CNNs.
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality
This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC).
Low-complexity CNNs for Acoustic Scene Classification
However, CNNs are resource hungry due to their large size and high computational complexity.
Low-complexity CNNs for Acoustic Scene Classification
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC).
Efficient CNNs via Passive Filter Pruning
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.
Compressing audio CNNs with graph centrality based filter pruning
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.
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.
