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Porn streamer audio recognition based on deep learning and random Forest

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Abstract

The existing porn streamers audio recognition algorithms show poor performance in increasingly complex network environment. To resolve this problem, a porn streamer audio recognition algorithm based on deep learning and random forest is proposed. In this algorithm, a more stable complementary feature is first proposed, which consists of Log Mel Spectrum (LMS), Mel Frequency Cepstrum Coefficient (MFCC) and Gammatone Frequency Cepstrum Coefficient (GFCC), and the Dual-Path Fused Transformer Net (DPFTNet) network structure is then proposed for sound classification, which parallelizes the two main modules of the Swin Transformer, so that more feature details can be retained. Finally, the random forest is utilized to identify porn streamer. The experimental results show that this algorithm has higher recognition accuracy than the comparison algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61971016).

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Shangfeng Liu, Ruwei Li, Qiuyan Li & Jingyu Zhao

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  1. Shangfeng Liu
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  2. Ruwei Li
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Correspondence to Ruwei Li.

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Liu, S., Li, R., Li, Q. et al. Porn streamer audio recognition based on deep learning and random Forest. Appl Intell 53, 18857–18867 (2023). https://doi.org/10.1007/s10489-023-04491-x

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