Multi-scale and multi-task learning for human audio forensics based on convolutional networks

Congrui Yin

doi:10.1117/12.2681344

8 June 2023 Multi-scale and multi-task learning for human audio forensics based on convolutional networks

Congrui Yin

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127074I (2023) https://doi.org/10.1117/12.2681344
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Audio forgery has become a growing challenge with rising social media usage. To reduce complexity and enable accurate recognition, this paper proposes an EfficientNet model with multi-scale spectral image features and multi-task learning for two scenarios: audio forgery and speaker identification. Audio signals are enhanced and expanded by multiscale audio segmentation, then different feature extraction techniques are used to transform them into spectral image features. Image recognition is performed by EfficientNet, and the two-stage voting method is used for multi-task learning. Experiments on Ali Tianchi datasets 1 & 2 show optimal results under Spectrogram-based spectral image features. Compared to multi-scale ResNet-152, Inception-v3, and Inception-Resnet-v2 models, the accuracy of the binary classification task was improved by 8.26%, 9.64%, and 2.46%, respectively (dataset 2, 12.80%, 11.60%, and 2.44%); the accuracy of the multi-classification task was improved by 9.61%, 8.56%, and 1.97%, respectively (dataset 2, 12.80%, 11.60%, and 2.44%).

Citation Download Citation

Congrui Yin "Multi-scale and multi-task learning for human audio forensics based on convolutional networks", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127074I (8 June 2023); https://doi.org/10.1117/12.2681344

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KEYWORDS

Machine learning

Feature extraction

Data modeling

Image segmentation

Forensic science

Education and training

Denoising

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