  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.
