Audio Porn Scanner Filters Content By Listening for “Sexual Screams or Moans”

A pair of South Korean electrical engineers have worked out a new porn filter that analyzes audio for tell-tale signs of things you really shouldn’t be watching at work. By using sound, they avoid the problem of visual porn-identifiers (pornifiers) that can get tricked by any expanse of skin, as in closeups of the face or other not-inappropriate body parts.

The engineers started by making spectrograms, basically a visual representation of a sound clip, of lots of different kinds of audio, including music, non-porn video, and porn. By analyzing these spectrograms, they figured out that pornographic audio has a few unique qualities that make it fairly easy to recognize: Regular speech is low-pitched, music has lots of different pitches, and both tend to be fairly constant. But the porn spectrograms showed a high pitch that changes often and also repeats itself.

Using this data, the engineers developed software that could identify porn correctly about 93% of the time. There were some hiccups; apparently laugh-tracked sitcoms sound like porn (weird fact!) and porn can sometimes sneak by the censors by using background music. Of course, image-recognition porn identifiers are just about as accurate and require much less time to analyze (a single frame, versus the audio identifier’s need for a longer clip). But it doesn’t necessarily need to be one or the other: Some see a potential to combine the audio and visual identifiers into one super-detector, a gauntlet through which no porn can pass.

New Scientist

 

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Dan Nosowitz is a freelance writer and editor who has written for Popular Science, The Awl, Gizmodo, Fast Company, BuzzFeed, and elsewhere. He holds an undergraduate degree from McGill University and currently lives in Brooklyn, because he has a beard and glasses and that's the law. You can follow him on Twitter.