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arXiv · 2210.15283

On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors

Zaharah Bukhsh·Aaqib Saeed
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Abstract

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.

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Zaharah Bukhsh, Aaqib Saeed. 2023-02-25. On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors. https://arxiv.org/abs/2210.15283

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