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Optimizing a-DCF for Spoofing-Robust Speaker Verification

IEEE signal processing LETTERS 2025年 32卷 1081-1085页

作者： Kurnaz, Oguzhan Mishra, Jagabandhu Kinnunen, Tomi H. Hanilci, Cemal Univ Eastern Finland Joensuu 80130 Finland Bursa Tech Univ TR-16310 Bursa Turkiye

Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. We propose a spoofing-robust ASV system optimized directly for the recently introduced architecture-agnostic detection cost function (a-DCF), which allows targeting a desired trade-off between the contradicting aims of user convenience and robustness to spoofing. We combine a-DCF and binary cross-entropy (BCE) with a novel straightforward threshold optimization technique. Our results with an embedding fusion system on ASVspoof2019 data demonstrate relative improvement of 13% over a system trained using BCE only (from minimum a-DCF of 0.1445 to 0.1254). Using an alternative non-linear score fusion approach provides relative improvement of 43% (from minimum a-DCF of 0.0508 to 0.0289).

关键词： Measurement Costs Training Cost function signal processing algorithms Error analysis Security Robustness Computer architecture Training data a-DCF spoofing-robust speaker verification

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Blind Recognition Algorithm of RS-SPC Concatenated Codes Based on Single-Error Correction

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IEEE signal processing LETTERS 2025年 32卷 101-105页

作者： Wu, Zhuolun Zhang, Wei Wang, Yihan Zhang, Yushan Liu, Yanyan Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Nankai Univ Coll Elect Informat & Opt Engn Tianjin 300071 Peoples R China Tianjin Univ Sch Architecture Tianjin 300072 Peoples R China

This letter presents an RS-SPC concatenated code blindrecognition algorithm based on the single-error correction. The algorithm corrects the least reliable bit of the single parity check (SPC) codewords based on the parity check characteristics, thereby increasing correct Reed-Solomon (RS) codewords and laying the foundation for improving the recognition probability. In addition, this algorithm combines threshold judgement with the matrix recording method, thereby eliminating unnecessary iterative operations under the condition that accurate recognition is possible. At the same time, it employs probability theory as a theoretical basis to quantify the degree of dispersion of the data through sample variance. The experimental results demonstrate that the recognition probability of this algorithm is superior to that of all other algorithms. When the codeword error rate (CER) is 0.5, RS(15,9)-SPC(4,3) still has a recognition probability of 20%. For RS(255,239)-SPC(8,7), the gain of the proposed algorithm exceeds 1.3dB compared to the upper bound of the recognition probability.

关键词： Codes signal processing algorithms Concatenated codes Polynomials Encoding Parity check codes Symbols Voltage Decoding Probability Reed-Solomon codes blind recognition Galois field Fourier transform concatenated code

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DOA Estimation of Coherent signals Exploiting Forward/Backward Convolutional Kernels

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IEEE signal processing LETTERS 2025年 32卷 876-880页

作者： Zhao, Jun Dong, Xudong Zhang, Han Sun, Meng China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Coll Commun Engn Hangzhou 310018 Peoples R China Nanjing Univ Posts & Telecommun Coll Commun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China

The traditional subspace-based algorithms in the process of coherent direction of arrival (DOA) estimation get in trouble because of the rank loss of the signal covariance matrix. To this end, this paper introduces a forward/backward convolution kernel (FBCK) method, which not only reconstructs the signal covariance matrix and its diagonal elements, but also efficiently solves the signal coherence problem by utilizing the moving array technique. More precisely, the FBCK operation is applied to the signal space matrix at a given instant and utilizes the forward/backward convolution kernel to recover the rank corresponding to the number of signals without loss of the arrays' aperture. In a comparison evaluation with state-of-the-art spatial smoothing methods (including MSSP, SSP, ESS, ESS-SS, SSS and ASS), the proposed FBCK algorithm demonstrates excellent estimation capabilities in terms of snapshot number and signal-to-noise ratio (SNR), thus providing a robust and effective solution for DOA estimation in coherent signal environments.

关键词： Covariance matrices Direction-of-arrival estimation Vectors Estimation signal processing algorithms Convolution Array signal processing Smoothing methods Kernel Sensor arrays Coherent signals DOA estimation convolution kernel signal space matrix

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A Target Parameters Estimation Method for CP-OTFS-Based Terrestrial Radar System

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 233-237页

作者： Miao, Qilong Liang, Jing Zhang, Ge Han, Jiachen Univ Elect Sci & Technol China Chengdu 610056 Peoples R China

Orthogonal time-frequency space (OTFS) modulation, which exhibits significant advantages in high-speed mobile communication scenarios, holds strong potential in the field of radar applications. The existing state-of-the-art target parameter estimation methods for cyclic prefix OTFS (CP-OTFS) based radar systems suffer from inaccuracies in target scattering coefficient (TSC) estimation under conditions of multiple targets, low bandwidth and short duration. In this letter, the equivalent matched filter (EMF) algorithm is designed. Compared to the matched filter (MF) algorithm, EMF intuitively explains the input-output relationship of CP-OTFS based radar, but it remains susceptible to interference from multiple targets. In order to mitigate this impact, the two-stage matched filter-based minimum mean squared error (TS-MF-MMSE) algorithm, a refined TSC estimation algorithm for terrestrial CP-OTFS based radar system is proposed. Simulation results demonstrate the superior performance of proposed algorithm in TSC estimation in comparison with EMF and the state-of-the-art.

关键词： Radar Estimation signal processing algorithms Symbols Matched filters Receivers OFDM Delays Wireless communication Spaceborne radar CP-OTFS target parameter estimation JRC minimum mean squared error

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TAML-Adapter: Enhancing Adapter Tuning Through Task-Agnostic Meta-Learning for Low-Resource Automatic Speech Recognition

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IEEE signal processing LETTERS 2025年 32卷 636-640页

作者： Liu, Yunpeng Yang, Xukui Zhang, Jiayi Xi, Yangli Qu, Dan Univ Informat Engn Sch Informat Syst Engn Dept Artificial Intelligence Zhengzhou 450000 Peoples R China Lab Adv Comp & Intelligence Engn Wuxi 214000 Peoples R China Zhengzhou Univ Zhengzhou 450000 Peoples R China

Parameter-efficient fine-tuning of pre-trained multilingual speech models can significantly enhance the speech recognition performance of target languages. However, traditional parameter-efficient fine-tuning methods, such as adapter tuning, often face challenges related to random initialization. This can lead to suboptimal performance when adapting to languages with limited resources. To address this issue, this letter introduces TAML-Adapter, which utilizes the Task-Agnostic Meta-Learning algorithm to initialize the parameters of the adapters before fine-tuning in target low-resource languages. Comprehensive experiments conducted on the Common Voice and Fleurs datasets highlight the superior performance of TAML-Adapter in five languages with limited resources. In addition, the TAML-Adapter demonstrates superior generalizability and extensibility compared to similar competing methods.

关键词： Adaptation models Metalearning Speech recognition Tuning Data models signal processing algorithms Multilingual Training Computational modeling Transformers Automatic speech recognition pre-trained multilingual speech models parameter-efficient fine-tuning Task-Agnostic Meta-Learning

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Momentum-Based Iterative Hard Thresholding Algorithm for Sparse signal Recovery

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IEEE signal processing LETTERS 2025年 32卷 1346-1350页

作者： Jin, Wen Xie, Lie-Jun Ningbo Univ Sch Math & Stat Ningbo 315211 Peoples R China

The iterative hard thresholding (IHT) algorithm is widely used for recovering sparse signals in compressed sensing. Despite the development of numerous variants of this effective algorithm, its convergence rate and accuracy in finding the optimal solution still have room for enhancement. Aiming at this issue, we propose a momentum-based iterative hard thresholding (MIHT) algorithm by introducing a new iterative search direction derived from the momentum method, which uses historical iteration information to refine the search direction and thereby accelerate convergence. We establish a sufficient condition, in terms of (3s)-order restricted isometry constant, to guarantee the convergence of MIHT. Excitingly, numerical experiments demonstrate that MIHT possesses an excellent recovery success rate and outperforms a wide range of existing IHT variants.

关键词： signal processing algorithms Vectors Iterative algorithms Convergence Thresholding (Imaging) Indexes Optimization Training Data mining Compressed sensing iterative hard thresholding momentum method restricted isometry constant search direction

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Scene Inference Using Saliency Graphs With Trust-Theoretic Semantic Information Encoding

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IEEE signal processing LETTERS 2025年 32卷 256-260页

作者： Meena, Preeti Kumar, Himanshu Yadav, Sandeep Indian Inst Technol Jodhpur Discipline Elect Engn Jodhpur 342030 India

Scene inference refers to the identification of the scene from a given set of scene representations such as images. A saliency graph of a scene contains scene-defining objects along with semantic information between them in the graph representation. Existing methods consider the entire scene with uniform weighting to all semantic information for scene inference, resulting in a suboptimal performance. This letter presents an optimal edge weight estimation using the trust theoretic framework to encode semantic information effectively in saliency graphs. We have utilized the notion of converged global absolute trust in saliency scores of salient objects to compute the weighting of semantic information. Experimental results highlight the efficacy of the proposed method.

关键词： Peer-to-peer computing Semantics Estimation Symmetric matrices Image edge detection signal processing algorithms Inference algorithms Eigenvalues and eigenfunctions Visualization Accuracy Scene inference saliency graph semantic information trust graph P2P

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Radar signal Sorting via Graph Convolutional Network and Semi-Supervised Learning

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IEEE signal processing LETTERS 2025年 32卷 421-425页

作者： Li, Ziying Fu, Xiongjun Dong, Jian Xie, Min Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Hebei Peoples R China

As a key technology in radar reconnaissance systems, radar signal sorting aims to separate multiple radar pulses from an interleaved pulse stream. Supervised signal sorting methods based on deep learning depend on a large volume of training data to optimize model parameters. However, acquiring labeled pulses in practice is challenging. In this letter, a semi-supervised learning (SSL) framework is proposed to address this issue. First, a Self-Organizing Map (SOM) is used to learn the spatial distribution of impulse features, and an anchor graph is constructed based on SOM nodes. A pseudo-label set is then generated using the SOM based on pulse discrepancy information. Finally, a three-layer Weighted Residual Graph Convolutional Network (WRGCN) is designed for signal sorting, with its parameters pre-trained on pseudo-labels and fine-tuned with a limited number of true labels. Experiments on a simulated radar pulse dataset demonstrate that this framework outperforms several existing methods for radar signal sorting with limited labeled pulses.

关键词： Radar Sorting Vectors Self-organizing feature maps Training Spaceborne radar Semisupervised learning Deep learning Training data signal processing algorithms Radar signal sorting graph convolutional network pseudo-labels semi-supervised learning

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Iterative Closest Point via MultiKernel Correntropy for Point Cloud Fine Registration

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IEEE signal processing LETTERS 2025年 32卷 766-770页

作者： Yi, Hao Hu, Limei Chen, Feng Ren, Xiaoping Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China

The Iterative Closest Point (ICP) method, primarily used for transformation estimation, is a crucial technique in 3D signal processing, especially for point cloud fine registration. However, traditional ICP is prone to local optima and sensitive to noise, especially when there is no good initialization. Based on the observation that registration errors typically exhibit a multimodal distribution under large rotational offsets and noisy environments, the MultiKernel Correntropy (MKC), which can estimate the registration error distribution, is introduced to provide global information for ICP. Moreover, since MKC consists of multiple Gaussian kernels, it can effectively resist most of the noise. A MultiKernel Correntropy based Iterative Closest Point (MKCICP) is proposed. Extensive experiments on both simulated and real-world datasets show that MKCICP achieves better performance compared to other related methods in challenging scenarios involving large rotational angles, low partial overlap, and high noise levels.

关键词： Point cloud compression Noise Iterative methods Optimization Kernel Vectors signal processing algorithms Mathematical models Loss measurement Linear programming MultiKernel Correntropy (MKC) Iterative Closest Point (ICP) transformation estimation signal processing global optimum point cloud registration

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Centroid-Free K-Means With Balanced Clustering

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IEEE signal processing LETTERS 2025年 32卷 1191-1195页

作者： Meng, Bin Li, Fangfang Yang, Fan Gao, Quanxue Jilin Police Coll Dept Informat Secur Technol Changchun 130117 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Changchun Humanities & Sci Coll Dept Chinese Language & Literature Changchun 130117 Peoples R China

Currently, a wide array of clustering algorithms have emerged, yet many approaches rely on K-means to detect clusters. However, K-means is highly sensitive to the selection of the initial cluster centers, which poses a significant obstacle to achieving optimal clustering results. Moreover, its capability to handle nonlinearly separable data is less than satisfactory. To overcome the limitations of traditional K-means, we draw inspiration from manifold learning to reformulate the K-means algorithm into a new clustering method based on manifold structures. This method not only eliminates the need to calculate centroids in traditional approaches, but also preserves the consistency between manifold structures and clustering labels. Furthermore, we introduce the l(2,1)-norm to naturally maintain class balance during the clustering process. Additionally, we develop a versatile K-means variant framework that can accommodate various types of distance functions, thereby facilitating the efficient processing of nonlinearly separable data. The experimental results of several databases confirm the superiority of our proposed model.

关键词： Clustering algorithms Manifolds Manifold learning signal processing algorithms Optimization Linear matrix inequalities Kernel Training Geometry Data mining Clustering l(2,1)-norm manifold learning K-means

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