![]() IEEE Signal Processing Letters, In Press, 2017. Finally, we examine the influence of each augmentation on the model’s classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.ĭeep Convolutional Neural Networks and Data Augmentation For Environmental Sound Classification Various neuroimaging techniques can be used to investigate how the brain processes sound. We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation. Auditory EEG decoding challenge 2024: ICASSP 2024. Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification. Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture. This study has two primary contributions: first, we propose a deep convolutional neural network architecture for environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This means the journal is among the top 17% in the science branch of Physical Sciences.The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. ![]() ![]() This Journal is the 1939 th out of 11,479 Physical Sciences journals. This Journal is the 889 th out of 4,401 Applied Sciences journals. This means the journal is among the top 11% in the science branch of Formal Sciences. To address this issue, in this letter, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. This Journal is the 316 th out of 2,945 Formal Sciences journals. Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. ![]() This means the journal is among the top 9% in the discipline of Mathematics. This Journal is the 178 th out of 2,015 Mathematics journals. ![]() This means the journal is among the top 19% in the discipline of Electrical Engineering. This Journal is the 106 th out of 581 Electrical Engineering journals. This means the journal is among the top 12% in the sub-discipline of Applied Mathematics. How Influential is IEEE Signal Processing Letters? IEEE Signal Processing Letters is the 97 th out of 837 Applied Mathematics journals. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years. Re-Draw The graph shows the changes in the impact factor of IEEE Signal Processing Letters and its the corresponding percentile for the sake of comparison with the entire literature. 2, with the estimated LE being about 0.90, the same as the known value in the literature. ![]()
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