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ERANNs: Efficient residual audio neural networks for audio pattern recognition. / Verbitskiy, Sergey; Berikov, Vladimir; Vyshegorodtsev, Viacheslav.
в: Pattern Recognition Letters, Том 161, 09.2022, стр. 38-44.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - ERANNs: Efficient residual audio neural networks for audio pattern recognition
AU - Verbitskiy, Sergey
AU - Berikov, Vladimir
AU - Vyshegorodtsev, Viacheslav
N1 - Funding Information: This work was partly supported by NVIDIA Inception Program for AI Startups; FASIE grant 0060584; RFBR grant 19-29-01175. We thank the anonymous reviewers, whose suggestions helped improve the manuscript quality. Publisher Copyright: © 2022 Elsevier B.V.
PY - 2022/9
Y1 - 2022/9
N2 - Audio pattern recognition (APR) is an important research topic and can be applied to several fields related to our lives. Therefore, accurate and efficient APR systems need to be developed as they are useful in real applications. In this paper, we propose a new convolutional neural network (CNN) architecture and a method for improving the inference speed of CNN-based systems for APR tasks. Moreover, using the proposed method, we can improve the performance of our systems, as confirmed in experiments conducted on four audio datasets. In addition, we investigate the impact of data augmentation techniques and transfer learning on the performance of our systems. Our best system achieves a mean average precision (mAP) of 0.450 on the AudioSet dataset. Although this value is less than that of the state-of-the-art system, the proposed system is 7.1x faster and 9.7x smaller. On the ESC-50, UrbanSound8K, and RAVDESS datasets, we obtain state-of-the-art results with accuracies of 0.961, 0.908, and 0.748, respectively. Our system for the ESC-50 dataset is 1.7x faster and 2.3x smaller than the previous best system. For the RAVDESS dataset, our system is 3.3x smaller than the previous best system. We name our systems “Efficient Residual Audio Neural Networks”.
AB - Audio pattern recognition (APR) is an important research topic and can be applied to several fields related to our lives. Therefore, accurate and efficient APR systems need to be developed as they are useful in real applications. In this paper, we propose a new convolutional neural network (CNN) architecture and a method for improving the inference speed of CNN-based systems for APR tasks. Moreover, using the proposed method, we can improve the performance of our systems, as confirmed in experiments conducted on four audio datasets. In addition, we investigate the impact of data augmentation techniques and transfer learning on the performance of our systems. Our best system achieves a mean average precision (mAP) of 0.450 on the AudioSet dataset. Although this value is less than that of the state-of-the-art system, the proposed system is 7.1x faster and 9.7x smaller. On the ESC-50, UrbanSound8K, and RAVDESS datasets, we obtain state-of-the-art results with accuracies of 0.961, 0.908, and 0.748, respectively. Our system for the ESC-50 dataset is 1.7x faster and 2.3x smaller than the previous best system. For the RAVDESS dataset, our system is 3.3x smaller than the previous best system. We name our systems “Efficient Residual Audio Neural Networks”.
KW - Audio pattern recognition
KW - Audio tagging
KW - Residual convolutional neural networks
KW - Sound classification
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85134814345&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2022.07.012
DO - 10.1016/j.patrec.2022.07.012
M3 - Article
AN - SCOPUS:85134814345
VL - 161
SP - 38
EP - 44
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
ER -
ID: 36710419