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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Longwei Cao Jiwei Qin Xuzi Zhang College of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Schemes (or Ponzi Contracts) and have caused huge financial losses. Current Ponzi contract detection models face three main challenges: simple opcode sequence processing does not effectively distinguish Ponzi from non-Ponzi contracts, single-feature-based models lack accuracy, and reliance on transaction records hinders early detection. To address these issues, this paper proposes a Multi-Feature Ponzi Scheme detection Model (MFDPonzi). MFDPonzi tracks the changes in stack, memory, and storage parameters during the execution of smart contracts, reconstructing opcode sequences and extracting diverse features, including semantic and developer features. Finally, a multi-feature fusion algorithm is used to enhance model stability. Additionally, MFDPonzi can identify Ponzi contracts at the early stage of smart contract creation without relying on transaction data. Experimental results show that MFDPonzi achieves an 85.9% recall and an 88.7% F-score on Ethereum smart contracts, outperforming baselines in both performance and robustness.

关键词： Biological system modeling Smart contracts signal processing algorithms Feature extraction Robustness Stability analysis Blockchains Logic Speech processing Thermal stability

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Difference Bonds Consistency and Complementarity to Enhance Multimodal Representation Learning

Difference Bonds Consistency and Complementarity to Enhance ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Congbing He Sensen Song Zhenhong Jia Hui Zhao Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal representation learning, existing research has primarily focused on exploring modal consistency and modal complementarity, while overlooking the positive role of modal difference. Moreover, modal difference establishes a bonding relationship between modal consistency and modal complementarity. However, existing algorithms lack the study of this relationship, resulting in an accuracy that still needs to be improved on multimodal classification tasks. To tackle the above issues, We propose a novel multimodal representation learning framework. It enhances multimodal representation learning by modal difference constructing connecting bonds of modal consistency and modal complementarity. We conducted experiments on two widely used multimodal emotion recognition datasets, IEMOCAP and MELD. The results demonstrate that our method outperforms existing multimodal representation learning approaches in terms of accuracy on the multimodal emotion recognition task.

关键词： Representation learning Emotion recognition Accuracy signal processing algorithms Speech recognition signal processing Acoustics Classification algorithms Speech processing Bonding

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DSOD-YOLO: A lightweight dual feature extraction method for small target detection

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Digital signal processing: A Review Journal 2025年 164卷

作者： Nie, Yuan Lai, Huicheng Gao, Guxue The College of Computer Science and Technology Xinjiang University Urumqi830017 China The Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

As object detection techniques advance, large-object detection has become less challenging. However, small-object detection remains a significant hurdle. DSOD-YOLO is a lightweight small-object detection network based on YOLOv8, designed to balance detection accuracy with model efficiency. To accurately detect small objects, the network employs a dual-backbone feature extraction architecture, which enhances the extraction of small-object details. This addresses the issue of detail loss in deep models. Additionally, a Channel-Scale Adaptive Module (FASD) is introduced to adaptively select feature channels and image sizes based on the required feature information. This helps mitigate the problem of sparse feature information and information loss during feature propagation for small objects. To strengthen contextual information and further improve small-object detection, a lightweight Context and Spatial Feature Calibration Network (CSFCN) is integrated. CSFCN performs context correction and spatial feature calibration through its two core modules, Context Feature Calibration (CFC) and Spatial Feature Calibration (SFC), based on pixel context similarity and channel dimensions, respectively. To reduce model complexity, the network undergoes a pruning process, achieving lightweight small-object detection. Furthermore, knowledge distillation is employed, with a large model acting as a teacher network to guide DSOD-YOLO, leading to further accuracy improvements. Experimental results demonstrate that DSOD-YOLO outperforms state-of-the-art algorithms like YOLOv9 and YOLOv10 on multiple small-object datasets. Additionally, a new small-object dataset (SmallDark) is created for low-light conditions, and the proposed method surpasses existing algorithms on this custom dataset. © 2025 Elsevier Inc.

关键词： Image enhancement

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Prompt Fusion and Aspect-Oriented Filtration for Aspect-Based Multimodal Sentiment Analysis

Prompt Fusion and Aspect-Oriented Filtration for Aspect-Base...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiachang Sun Xiuhong Li Fuxian Zhu School of Computer Science and Technology XinJiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Economics and Management XinJiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, aspect-based multimodal sentiment analysis (ABMSA) remains a highly hot research field, aiming to leverage various modalities such as images and text to determine the sentiment orientation of viewpoint entities. Although deep learning methods have made significant progress in this field, some challenges still exist: Image information unrelated to the viewpoint entity can introduce noise, insufficient interaction during modal fusion. To solve these problems, we propose a novel ABMSA model with prompt fusion and noise filtration. Specifically, we introduced an Image Noise Filtering (INF) module that can filter the extracted image features to reduce the noise introduced by the irrelevant between viewpoint entity and the image. Additionally, we implemented Multimodal Prompt Fusion (MPF) module to enhance the interaction between modalities, addressing the issue of minimal effective features related to viewpoint entity during the fusion stage of text and image features. Experimental results show that the model outperforms the baseline model on two public datasets, Twitter-2015 and Twitter-2017, demonstrating that our proposed approach effectively enhances the accuracy of aspect-based multimodal sentiment analysis tasks.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Filtration Noise Feature extraction Information filters Acoustics Speech processing

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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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Small Target Insect detection Based on Improved YOLOv8n

Small Target Insect Detection Based on Improved YOLOv8n

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jianyu Shi Yuan Jia Gang Zhou Jiajia Wang Zhenhong Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory School of Statistics Renmin University of China Beijing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Insect pests greatly affect the growth and harvest of crops, and accurate identification of insect species is very important to the agricultural industry. The insect dataset has the problem that the target is too small to locate, detect, and identify. To solve this problem, We designed a modified YOLOv8n for detecting small target insects. First, we add a small object detection head to the network structure of the YOLOv8n (You Only Look Once) model. Then, we improve C2f and Conv in YOLOv8n, construct C2f-RFAConv by adding Receptive-Field Attention Convolutional Operation (RFAConv) into C2f for the first time, and replace the original convolution with RFAConv for feature extraction. Finally, we improve Wise-IoU (WIoU) loss function to solve the problem of small object detection difficulty. When the number of parameters is reduced, the performance of our model YOLOv8n-Improved on the Yellow Sticky Traps dataset and self-built dataset is greatly improved compared with YOLOv8n, and it also has great advantages compared with other models in the YOLO series. This work plays an important role in our efforts to achieve intelligent pest management in agriculture.

关键词： YOLO Smart agriculture Accuracy Head Convolution Insects signal processing algorithms Pesticides Feature extraction Speech processing

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MRLCD-A: Lag-aware alignment for multivariate time series forecasting in multiple scenarios

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Information processing and Management 2025年第5期62卷

作者： Sun, Dezhi Qin, Jiwei Zhang, Zihao Qin, Xizhong Zhang, Huiguo School of Computer Science and Technology Xinjiang University Xinjiang Uygur Autonomous Region Urumqi 830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China School of Mathematics and System Science Xinjiang University Xinjiang Uygur Autonomous Region Urumqi 830046 China

In multivariate time series forecasting tasks, the varying degrees of lag relationships among multivariate data significantly increase the complexity of accurate predictions. A model must effectively capture long-term dependencies and address intricate lag correlations to achieve reliable long-term forecasting. This paper proposes a novel Multivariate Rolling Lag Correlation detection-Alignment (MRLCD-A) method to tackle these challenges. The method identifies rolling correlations, calculates lag distances in multivariate sequence inputs, and aligns the lagged variables accordingly. Multivariate Time Series (MTS) forecasting uses a Channel Dependency (CD) approach. Experiments on time series datasets across various scenarios, including electricity, weather, exchange rates, and atmospheric carbon concentrations, demonstrate that the proposed method outperforms state-of-the-art models in forecasting general multivariate time series and predicting long-term time series data in real-world environments. © 2025 Elsevier Ltd

关键词： Prediction models

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Conformer-Based Diffusion Model

ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Confor...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ke Chen Zhihua Huang Liang He Yonghong Yan School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-Switching (CS) Text-To-Speech (TTS) models have gained attention due to the increasing prevalence of multilingual communication. However, existing models struggle to meet the growing demand for personalized CS TTS, particularly in Zero-Shot (ZS) scenarios where the model should generate speech for an unseen speaker from reference speech. In this paper, we propose ZCS-CDiff, a zero-shot code-switching TTS system with the Conformer-based diffusion model. We disentangle speech features and use the diffusion model to precisely model these disentangled attributes, resulting in high-quality ZS CS speech. Additionally, we introduce a Conformer-based WaveNet as the denoising network within the diffusion model to further improve the accuracy of modeling different attributes. We also designed a speaker-assist module to help the model better handle speaker information extracted from the reference speech, resulting in CS speech with higher speaker similarity. Experimental results and ablation studies demonstrate that the ZS CS speech generated by ZCS-CDiff has good speech naturalness, intelligibility, and speaker similarity while confirming the effectiveness of our design choices. Audio samples are available 1 .

关键词： Training Adaptation models Accuracy Speech coding Noise reduction Speech enhancement signal processing Diffusion models Probabilistic logic Text to speech

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