Title: IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer Show Chinese title

Title: IMPACT：通过声学认知变压器的工业机器感知

Abstract: Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation Sounds), a large-scale open-access dataset. DINOS comprises over 74,149 audio samples (exceeding 1,093 hours) collected from various industrial acoustic scenarios. We also present IMPACT (Industrial Machine Perception via Acoustic Cognitive Transformer), a novel foundation model for industrial machine sound analysis. IMPACT is pretrained on DINOS in a self-supervised manner. By jointly optimizing utterance and frame-level losses, it captures both global semantics and fine-grained temporal structures. This makes its representations suitable for efficient fine-tuning on various industrial downstream tasks with minimal labeled data. Comprehensive benchmarking across 30 distinct downstream tasks (spanning four machine types) demonstrates that IMPACT outperforms existing models on 24 tasks, establishing its superior effectiveness and robustness, while providing a new performance benchmark for future research. Show Chinese abstract

Abstract: 工业机器的声学信号为异常检测、预测性维护和操作效率提升提供了有价值的见解。 然而，现有的任务特定的监督学习方法在不同工业场景中往往扩展性差且难以泛化，这些场景的声学特征与一般音频不同。 此外，缺乏可访问的大规模数据集和针对工业音频的预训练模型，阻碍了社区驱动的研究和基准测试。 为了解决这些挑战，我们引入了DINOS（多样化的工业运行声音），一个大规模的开放数据集。 DINOS包含超过74,149个音频样本（超过1,093小时），从各种工业声学场景中收集而来。 我们还提出了IMPACT（通过声学认知变压器进行工业机器感知），一种用于工业机器声音分析的新基础模型。 IMPACT在DINOS上以自监督方式进行了预训练。 通过联合优化话语级和帧级损失，它能够捕捉全局语义和细粒度的时间结构。 这使其表示适合在少量标记数据的情况下对各种工业下游任务进行高效微调。 在30个不同的下游任务（涵盖四种机器类型）上的全面基准测试表明，IMPACT在24个任务上优于现有模型，确立了其优越的有效性和鲁棒性，同时为未来研究提供了新的性能基准。