arXiv subjects

Kunde Yang

Publications and source records attributed to Kunde Yang.

7 recordsLinked to original sources

arXiv2025

Angle-distance decomposition based on deep learning for active sonar detection

Underwater target detection using active sonar constitutes a critical research area in marine sciences and engineering. However, traditional signal processing methods face significant challenges in complex underwater environments due to noise, reverberation, and interference. To address these issues, this paper presents a deep learning-based active sonar target detection method that decomposes the detection process into separate angle and distance estimation tasks. Active sonar target detection employs deep learning models to predict target distance and angle, with the final target position determined by integrating these estimates. Limited underwater acoustic data hinders effective model training, but transfer learning and simulation offer practical solutions to this challenge. Experimental results verify that the method achieves effective and robust performance under challenging conditions.

eess.SP↗

arXiv2025

Hydrogen Bond Strength Dictates the Rate-Limiting Steps of Diffusion in Proton-Conducting Perovskites:A Critical Length Perspective

Identifying the rate-limiting step of proton migration in proton-conducting oxides is essential for assessing and regulating proton conductivity. Proton migration based on the Grotthuss mechanism involves both proton rotation and proton transfer, with the latter typically regarded as the rate-limiting step. However, a universal criterion for identifying the rate-limiting step remains to be established. Here, we perform a quantitative decomposition of the rotation and transfer barriers, revealing that the hydrogen bond to the acceptor oxygen dictates their energy barrier difference via the O$_i$-B-O$_f$ bending mechanism. Based on the energy difference associated with a one-order-of-magnitude variation in residence time, we propose the hydrogen bond length criterion for identifying the rate-limiting step across operating temperatures. Taking the 500 K criterion as an upper limit, when the hydrogen-bond length of systems falls below 2.05~\AA, proton rotation becomes competitive with transfer. Applied to a wider range of perovskite materials, this criterion predicts comparable rotation and transfer rates in cubic structures with small lattice constants, low-valent B-site doped systems with moderate ionic radii, and distorted orthorhombic structures. Our findings provide an atomic-scale insight into the proton migration mechanisms in perovskites, and offer practical guidance for optimizing and designing advanced proton-conducting electrolytes.

cond-mat.mtrl-sci↗

arXiv2023

A Robust ADMM-Based Optimization Algorithm For Underwater Acoustic Channel Estimation

Accurate estimation of the Underwater acoustic (UWA) is a key part of underwater communications, especially for coherent systems. The severe multipath effects and large delay spreads make the estimation problem large-scale. The non-stationary, non-Gaussian, and impulsive nature of ocean ambient noise poses further obstacles to the design of estimation algorithms. Under the framework of compressed sensing (CS), this work addresses the issue of robust channel estimation when measurements are contaminated by impulsive noise. A first-order algorithm based on alternating direction method of multipliers (ADMM) is proposed. Numerical simulations of time-varying channel estimation are performed to show its improved performance in highly impulsive noise environments.

eess.SP↗

arXiv2022

LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification

Although the security of automatic speaker verification (ASV) is seriously threatened by recently emerged adversarial attacks, there have been some countermeasures to alleviate the threat. However, many defense approaches not only require the prior knowledge of the attackers but also possess weak interpretability. To address this issue, in this paper, we propose an attacker-independent and interpretable method, named learnable mask detector (LMD), to separate adversarial examples from the genuine ones. It utilizes score variation as an indicator to detect adversarial examples, where the score variation is the absolute discrepancy between the ASV scores of an original audio recording and its transformed audio synthesized from its masked complex spectrogram. A core component of the score variation detector is to generate the masked spectrogram by a neural network. The neural network needs only genuine examples for training, which makes it an attacker-independent approach. Its interpretability lies that the neural network is trained to minimize the score variation of the targeted ASV, and maximize the number of the masked spectrogram bins of the genuine training examples. Its foundation is based on the observation that, masking out the vast majority of the spectrogram bins with little speaker information will inevitably introduce a large score variation to the adversarial example, and a small score variation to the genuine example. Experimental results with 12 attackers and two representative ASV systems show that our proposed method outperforms five state-of-the-art baselines. The extensive experimental results can also be a benchmark for the detection-based ASV defenses.

eess.AS↗

arXiv2022

Symmetric Saliency-based Adversarial Attack To Speaker Identification

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost.

cs.SD↗

arXiv2021

IF equation: a feature extractor for high-concentration time-frequency representation of mixed signals

High-concentration time-frequency (TF) representation provides a valuable tool for characterizing multi-component non-stationary signals. In our previous work, we proposed using an instantaneous frequency (IF) equation to sharpen the TF distribution, and experiments verified its effectiveness. In this paper, we systematically discuss why the IF equation-based TF analysis methods work and how to use the IF equation to improve TF sharpness. By the analysis of the properties of the IF equation, we prove that a good IF equation can unify the well-known IF and group delay estimators and provides an effective way to characterize the mixture of time-varying and frequency-varying signals. By discussing the post-processing techniques based on the IF equation, we can prove that many popular TF post-processing methods, such as the synchroextracting transform, the multi-synchrosqueezing transform, and the time extracting transform, fall into the IF equation-based category. We also propose a novel approach to combine different IF equations to minimize energy spreading based on local sparsity. Numerical simulations and practical experiments are presented to illustrate the performance of the proposed IF equation-based TF analysis method.

eess.SP↗

arXiv2021

High-resolution chirplet transform: from parameters analysis to parameters combination

The standard chirplet transform (CT) with a chirp-modulated Gaussian window provides a valuable tool for analyzing linear chirp signals. The parameters present in the window determine the performance of the CT and play a vital role in high-resolution time-frequency (TF) analysis. In this paper, we give the window shape analysis of the CT and compare it with the extension that employs a rotating Gaussian window by the fractional Fourier transform. The given parameters analysis provides theoretical guidance for developing high-resolution CT. We then propose a multi-resolution chirplet transform (MrCT) by combining multiple CTs with different parameter combinations. These are combined geometrically to obtain an improved TF resolution by overcoming the limitations of any single representation of the CT. By deriving a combined instantaneous frequency equation, we further develop a high-concentration TF post-processing approach to improve the readability of the MrCT. Numerical experiments on simulated and real signals verify the effectiveness of the proposed methods.

eess.SP↗