生物启发的机器与水下声学记录中的高效循环平稳分析
Bio-inspired efficient cyclostationary analysis in machine and underwater acoustic recordings
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中文总结 AI 辅助
本文提出一种基于CARFAC模型内毛细胞响应的生物启发方法，用于高效提取声学信号中的循环调制，在CWRU轴承和ShipsEar数据集上验证了其可靠性与计算效率。
中文摘要 AI 辅助
我们提出了一种生物启发的方法，利用具有快速压缩的非对称谐振器级联（CARFAC）模型的内毛细胞（IHC）响应，从声学信号中高效提取循环调制。我们进一步通过比较CARFAC-IHC响应与CARFAC基底膜（BM）滤波来研究IHC处理的贡献。此外，将CARFAC-IHC和CARFAC-BM方法与传统的FFT累积方法（FAM）、集成循环调制相干性（ICMC）以及包络调制噪声检测（DEMON）方法在凯斯西储大学（CWRU）轴承数据集和真实的ShipsEar工作船记录数据集上进行了基准测试。结果表明，该方法能够可靠地恢复特征循环分量，同时大幅降低传统循环平稳分析的计算负担。
英文摘要
We propose a bio-inspired approach that uses the inner-hair-cell (IHC) response of the Cascade of Asymmetric Resonators with Fast-Acting Compression (CARFAC) model to efficiently extract cyclic modulation from acoustic signals. We further investigate the contribution of IHC processing by comparing the CARFAC-IHC response with the CARFAC basilar-membrane (BM) filtering. Furthermore, the CARFAC-IHC and CARFAC-BM approach are benchmarked against conventional FFT Accumulation Method (FAM), Integrated Cyclic Modulation Coherence (ICMC), and Detection of Envelope Modulation On Noise (DEMON) approaches using the Case Western Reserve University (CWRU) bearing dataset and a real ShipsEar work-vessel recording dataset. The results demonstrate reliable recovery of characteristic cyclic components while substantially reducing the computational burden of conventional cyclostationary analysis.
发表机构
- Western Sydney University（西悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。
