用测试时自适应提升噪声环境下语音分类准确率
An Investigation of Test-time Adaptation for Audio Classification under Background Noise
- 在测试阶段仅用无标签数据动态调整模型，应对背景噪声带来的域偏移
- 改进版CoNMix在10dB骑行声下误差仅5.31%，3dB跑步水声下12.75%
- 首次系统研究TTA在音频分类中的应用，适合噪声场景下的模型部署
领域偏移是深度学习中的突出问题，导致预训练模型在测试数据上性能显著下降。本文针对背景噪声引起的语音分类领域偏移问题，采用测试时自适应（TTA）技术，在测试阶段仅利用未标注测试数据对预训练模型进行动态调整后再预测。我们比较了三种主流TTA方法：TTT、TENT和最新的CoNMix，并在AudioMNIST（AM）和SpeechCommands V1（SC）两个常用语音分类数据集上，评估其在不同噪声类型与强度下的表现。实验结果表明，改进后的CoNMix在领域偏移条件下表现最优：在10 dB骑行声噪声下，AM数据集错误率为5.31%；在3 dB跑步水声噪声下，错误率为12.75%。现有文献中未见类似研究，本工作首次系统探索了TTA在语音分类任务中应对噪声域偏移的应用价值。
原文摘要 · Abstract (English)
Domain shift is a prominent problem in Deep Learning, causing a model pre-trained on a source dataset to suffer significant performance degradation on test datasets. This research aims to address the issue of audio classification under domain shift caused by background noise using Test-Time Adaptation (TTA), a technique that adapts a pre-trained model during testing using only unlabelled test data before making predictions. We adopt two common TTA methods, TTT and TENT, and a state-of-the-art method CoNMix, and investigate their respective performance on two popular audio classification datasets, AudioMNIST (AM) and SpeechCommands V1 (SC), against different types of background noise and noise severity levels. The experimental results reveal that our proposed modified version of CoNMix produced the highest classification accuracy under domain shift (5.31% error rate under 10 dB exercise bike background noise and 12.75% error rate under 3 dB running tap background noise for AM) compared to TTT and TENT. The literature search provided no evidence of similar works, thereby motivating the work reported here as the first study to leverage TTA techniques for audio classification under domain shift.
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