提出均匀分布滤波器组音频特征，提升机器异常声音检测效果
An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models
- 设计等间距滤波器组，确保频段关注度均等
- 基于预训练模型实现无参数去冗余，提升检测性能
- 适合工业异常声音监测场景，无需调参
异常声音检测（ASD）旨在从机器声音中识别异常，受到学术界和产业界的广泛关注。然而，异常位置不确定以及声音中的噪声等冗余信息严重影响系统性能。本文提出一种滤波器组均匀分布的新型音频特征，确保音频所有频段获得同等关注，从而增强对机器异常声音的检测能力。此外，基于预训练模型，提出一种无参数特征增强方法，有效去除机器音频中的冗余信息。该无参数策略有助于在微调阶段将预训练任务的通用知识高效迁移至ASD任务。在DCASE 2024挑战赛数据集上的评估结果表明，所提方法显著提升了ASD性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。