Mitigation of multi-path propagation artefacts in acoustic targets with cepstral adaptive filtering

2025-12-11

AI Summary

In passive acoustic monitoring, multipath reflections and motion-induced artifacts severely degrade target signal quality; existing filtering methods lack robustness due to their neglect of environmental non-stationarity and medium heterogeneity. This paper proposes a cepstral-domain adaptive bandstop filtering method: for the first time, quefrency-intensity-driven dynamic bandwidth control is introduced for acoustic multipath suppression, enabling robust source–reflection separation in the time–frequency domain. The method integrates cepstral analysis, adaptive time–frequency filtering, and dynamic parameter adjustment to accommodate time-varying propagation conditions. Experiments on aircraft noise simulations show significant improvements in SNR, log-spectral distortion (LSD), and Itakura–Saito (IS) distance. For ship-type classification on DeepShip and VTUAD v2 datasets, Matthews correlation coefficient (MCC) increases by 2.28% and 2.62%, respectively. Moreover, target recognition accuracy and time-delay estimation precision are both notably enhanced.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Active Learning
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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), log-spectral distance (LSD), and Itakura-Saito (IS) distance 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.

Problem

• Separates target signals from reflections in spectrograms
• Improves acoustic target classification in multi-path environments
• Enhances signal-to-noise ratio and classification performance metrics

Innovation

• Adaptive band-stop filtering on cepstral coefficients
• Dynamic bandwidth adjustment based on quefrency intensity
• Improved signal separation in multi-path acoustic environments