# What Did I Just Hear? Detecting Pornographic Sounds in Adult Videos Using Neural Networks

arXiv: https://arxiv.org/abs/2209.03711 · HTML: https://mlsanity.com/paper/2209.03711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [A Comparison of deep learning methods for environmental sound](https://mlsanity.com/paper/1703.06902/) (2017)

## Built on by (most cited first, 1 of 1)

- [Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition](https://mlsanity.com/paper/2306.14517/) (2023)

## Abstract (arXiv)

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.