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.

Building an AI that could understand and speak back to me has always been a dream of mine. Ever since I saw Iron Man's Jarvis in action, the urge to actually try it became more intense. My dream has now become true. In this story I'll go through how to build an AI that can understand and speak back to you using astibob and golang. It will be able to repeat what you're saying. For teaching purposes we'll split abilities in different workers, but bare in mind that all abilities can be on the same worker if it makes more sense.