What Did I Just Hear? Detecting Pornographic Sounds in Adult Videos Using Neural Networks
This work addresses adult content filtering for platforms needing efficient audio-based detection, but it is incremental as it applies existing neural methods to a specific domain.
The paper tackled the problem of detecting pornographic sounds in adult videos by exploring neural architectures and acoustic features, finding that a CNN trained on log mel spectrograms achieved the best performance on the Pornography-800 dataset, with a voting segment-to-audio technique yielding top audio-level detection results.
Audio-based pornographic detection enables efficient adult content filtering without sacrificing performance by exploiting distinct spectral characteristics. To improve it, we explore pornographic sound modeling based on different neural architectures and acoustic features. We find that CNN trained on log mel spectrogram achieves the best performance on Pornography-800 dataset. Our experiment results also show that log mel spectrogram allows better representations for the models to recognize pornographic sounds. Finally, to classify whole audio waveforms rather than segments, we employ voting segment-to-audio technique that yields the best audio-level detection results.