Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid making confident predictions on OOD inputs as it can lead to potentially dangerous consequences. However, OOD detection largely remains an under-explored area in the audio (and speech) domain. This is despite the fact that audio is a central modality for many tasks, such as speaker diarization, automatic speech recognition, and sound event detection. To address this, we propose to leverage feature-space of the model with deep k-nearest neighbors to detect OOD samples. We show that this simple and flexible method effectively detects OOD inputs across a broad category of audio (and speech) datasets. Specifically, it improves the false positive rate (FPR@TPR95) by 17% and the AUROC score by 7% than other prior techniques.
@article{arxiv.2210.15283,
title = {On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors},
author = {Zaharah Bukhsh and Aaqib Saeed},
journal= {arXiv preprint arXiv:2210.15283},
year = {2023}
}
Comments
Accepted at ICASSP'23. Webpage: https://zaharah.github.io/ood_audio, Code: https://github.com/Zaharah/ood_audio