中文标题#
基于预训练模型的异常声音检测增强音频特征
英文标题#
An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models
中文摘要#
异常声音检测（ASD）旨在从机器中识别异常声音，并在学术界和工业界引起了广泛的研究兴趣。 然而，异常位置的不确定性以及机器声音中的大量冗余信息（如噪声）阻碍了 ASD 系统性能的提升。 本文提出了一种具有均匀分布间隔的滤波器组音频特征，确保音频中所有频率范围得到同等关注，从而增强了对机器声音中异常的检测。 此外，基于预训练模型，本文提出了一种无需参数的特征增强方法，以去除机器音频中的冗余信息。 认为这种无需参数的策略有助于在模型微调过程中将通用知识从预训练任务有效转移到 ASD 任务。 在 Detection and Classification of Acoustic Scenes and Events（DCASE）2024 挑战数据集上的评估结果表明，我们的方法显著提高了 ASD 性能。
英文摘要#
Anomalous Sound Detection (ASD) aims at identifying anomalous sounds from machines and has gained extensive research interests from both academia and industry. However, the uncertainty of anomaly location and much redundant information such as noise in machine sounds hinder the improvement of ASD system performance. This paper proposes a novel audio feature of filter banks with evenly distributed intervals, ensuring equal attention to all frequency ranges in the audio, which enhances the detection of anomalies in machine sounds. Moreover, based on pre-trained models, this paper presents a parameter-free feature enhancement approach to remove redundant information in machine audio. It is believed that this parameter-free strategy facilitates the effective transfer of universal knowledge from pre-trained tasks to the ASD task during model fine-tuning. Evaluation results on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge dataset demonstrate significant improvements in ASD performance with our proposed methods.
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