However, the computational complexity of computing the pairwise similarity matrix is high, particularly when a convolutional layer has many filters.
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC).
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.
This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC).
We propose a passive filter pruning framework, where a few convolutional filters from the CNNs are eliminated to yield compressed CNNs.
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.
In this paper, we study the problem of selecting high-value subsets of training data.
Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain.