用自适应倒谱滤波分离声学目标信号与多路径反射，提升分类准确率。
Mitigation of multi-path propagation artefacts in acoustic targets with adaptive cepstral filtering
- 基于倒谱系数动态调整带阻滤波器带宽，自适应抑制反射干扰。
- 在10~100米/秒速度下提升信噪比与频谱距离，船舶分类准确率提高2.28~2.62个百分点。
- 适合水下或复杂环境中的声学目标识别与时延估计任务。
被动声学传感是监测船舶、飞机等移动目标的低成本方案，但受多路径反射和运动诱发伪影影响，性能受限。现有滤波方法未能充分考虑环境特性或介质变化，难以有效分离源信号与反射成分。本文提出一种在频谱图中分离目标信号与反射的方法：通过自适应带阻滤波对倒谱系数进行时序处理，其带宽依据虚频率分量相对强度动态调整。该方法在模拟运动的飞机噪声中，于10至100米/秒速度范围内提升了信噪比（SNR）与对数谱距离（LSD）。在水下任务中，使DeepShip与VTUAD v2数据集上的船舶分类性能分别提升2.28和2.62个百分点的马修斯相关系数（MCC）。结果表明，该流程在多路径环境中具有提升声学目标分类与时延估计的潜力，未来将探索幅度保真与多传感器应用。
原文摘要 · Abstract (English)
Passive acoustic sensing is a cost-effective solution for monitoring moving targets such as vessels and aircraft, but its performance is hindered by complex propagation effects like multi-path reflections and motion-induced artefacts. Existing filtering techniques do not properly incorporate the characteristics of the environment or account for variability in medium properties, limiting their effectiveness in separating source and reflection components. This paper proposes a method for separating target signals from their reflections in a spectrogram. Temporal filtering is applied to cepstral coefficients using an adaptive band-stop filter, which dynamically adjusts its bandwidth based on the relative intensity of the quefrency components. The method improved the signal-to-noise ratio (SNR) and log-spectral distance (LSD) across velocities ranging from 10 to 100 metres per second in aircraft noise with simulated motion. It also enhanced the performance of ship-type classification in underwater tasks by 2.28 and 2.62 Matthews Correlation Coefficient percentage points for the DeepShip and VTUAD v2 datasets, respectively. These results demonstrate the potential of the proposed pipeline to improve acoustic target classification and time-delay estimation in multi-path environments, with future work aimed at amplitude preservation and multi-sensor applications.
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