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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

Journal of Shanghai Jiaotong University (Science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

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 Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1048-1063页

作者： Ye Tian Yuandong Feng Xingyi Zhang Changyin Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China School of Automation Southeast UniversityNanjing 210096China

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

关键词： Evolutionary computation fast clustering sparse multi-objective optimization super-large-scale optimization

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Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 27-43页

作者： Zhang, Chao Zhang, Jiahui Li, Wentao Castillo, Oscar Zhang, Jiayi School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China College of Artificial Intelligence Southwest University Chongqing400715 China Division of Graduate Studies and Research Tijuana Institute of Technology Tijuana22379 Mexico School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probabilistic rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficien

关键词： Bicycles

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Iteratively Refined Behavior Regularization for Offline Reinforcement Learning 38

Iteratively Refined Behavior Regularization for Offline Rein...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Ma, Yi Hao, Jianye Hu, Xiaohan Zheng, Yan Xiao, Chenjun School of Computer and Information Technology Shanxi University China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education China College of Intelligence and Computing Tianjin University China Noah's Ark Lab Huawei Canada The Chinese University of Hong Kong Shenzhen China

One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not, we anticipate that an algorithm should demonstrate its ability to learn an effective control policy that seamlessly aligns with the inherent distribution of offline data. Unfortunately, behavior regularization, a simple yet effective offline RL algorithm, tends to struggle in this regard. In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. Our key observation is that by iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement, while also implicitly avoiding querying out-of-sample actions to prevent catastrophic learning failures. We prove that in the tabular setting this algorithm is capable of learning the optimal policy covered by the offline dataset, commonly referred to as the in-sample optimal policy. We then explore several implementation details of the algorithm when function approximations are applied. The resulting algorithm is easy to implement, requiring only a few lines of code modification to existing methods. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks, clearly demonstrate its superiority over behavior regularization. © 2024 Neural information processing systems foundation. All rights reserved.

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The Method of processing Abnormal Values for Flight Test Data Based on Wavelet Transform 2

The Method of Processing Abnormal Values for Flight Test Dat...

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2nd International Conference on Electronic Information Engineering and Computer Technology, EIECT 2022

作者： Xu, Jingxiang Zuo, Lei Zhao, Zheng Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China State Key Laboratory of Experimental Physics and Computational Mathematics Beijing100076 China

ISBN: (纸本)9798350399561

In the flight test data, there usually are abnormal values due to uncertainties in acquisition, storage and transmission. processing the abnormal values is essential to ensure the accuracy of subsequent test data analysis. In this paper, we propose an abnormal value processing method based on wavelet transform for flight test data. We use the summation of high-frequency wavelet details as the characteristic quantity for abnormal value detection. Moreover, the detection threshold is constructed based on the idea of constant false alarm detection to locate and process the abnormal values. Simulation results show that the method can detect and process the abnormal values in the time-series flight test data fast and accurately. © 2022 IEEE.

关键词： Wavelet transforms

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MRA-Depth: Multi-Resolution Attention based Monocular Depth Estimation 5

MRA-Depth: Multi-Resolution Attention based Monocular Depth ...

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5th International Conference on Robotics and Computer Vision, ICRCV 2023

作者： Wen, Jing Niu, Lukuan School of Computer Information and Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China School of Computer Information and Technology Shanxi University Taiyuan China

ISBN: (纸本)9798350326369

Delicate feature would become weaker or even disappear as going through deeper network layer, which results in jagged edges of objects in the depth estimation. To address this problem, we propose a multi-resolution attention based monocular depth estimation by capturing and recalibrating the dependencies of channels between different resolutions, including: we develop a multi-resolution attention encoder by embedding resolution and spatial attention, so as to extract the higher quality features in encoder, and we design a reusable multi-resolution fusion model in decoder, in which different scale features are integrated and then extract multi-resolution attention maps, so as to enhance detail information locally and perspective structure features globally. Experimental results illustrate the proposed network is capable to predict more precise depth and sharper contour, and to outperforms related methods on the KITTI benchmark. © 2023 IEEE.

关键词： Network layers

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Spatially Informed Independent vector analysis for Source Extraction based on the convolutive Transfer Function Model 48

Spatially Informed Independent vector analysis for Source Ex...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wang, Xianrui Brendel, Andreas Huang, Gongping Yang, Yichen Kellermann, Walter Chen, Jingdong Northwestern Polytechnical University CIAIC and Shaanxi Provincial Key Laboratory of Artificial Intelligence Xi'an710072 China Friedrich-Alexander-Universität Multimedia Communications and Signal Processing Erlangen-Nürnberg Germany

ISBN: (纸本)9781728163277

Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the performance of those methods degrades quickly as the reverberation increases. The underlying reason is that those methods are derived based on the multiplicative transfer function model with a rank-1 assumption, which does not hold true if reverberation is strong. To circumvent this issue, this paper proposes to use the convolutive transfer function (CTF) model to improve the source extraction performance and develop a spatially informed IVA algorithm. Simulations demonstrate the efficacy of the developed method even in highly reverberant environments. © 2023 IEEE.

关键词： Transfer functions

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Deform U-Net: Unsupervised Deformable 3D Biomedical Image Registration Network

Deform U-Net: Unsupervised Deformable 3D Biomedical Image Re...

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Artificial intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yahu Yao Rong Lu Jun Wu Zaiyang Tao Lei Qu Key Laboratory of Computational Intelligence and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University (AHU) Hefei China Anhui University (AHU) Refei China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

We can focus on the challenges posed by traditional registration algorithms and the limitations of U-Net-based architectures in capturing sufficient semantic information due to their shallow coding layers with small receptive fields. We want to explore how newer architectures or modifications to existing ones could address these limitations. Inspired by introduced deformable convolutional networks, we plan to fuse deformable convolution into our proposed network structure. Building on its success in 2D target detection, we apply deformable convolution to 3D image registration networks. The Deform U-Net proposed in this paper can compensate for the shortcomings of existing registration networks by obtaining more semantic information with unsupervised training without Introduction of additional features. We conducted evaluations on two publicly available human brain MRI, specifically LPBA40 and IBSR18. On the LPBA40 and ISBR18 datasets, the DSC reached 68.82, 62.30, respectively. The experimental results show that the Deform U-N et image registration method outperforms the VoxelMorph image registration method.

关键词： Training Seminars Image registration Three-dimensional displays Convolution Architecture Semantics

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An Improved Optimal Transport Kernel Embedding Method with Gating Mechanism for Singing Voice Separation and Speaker Identification 48

An Improved Optimal Transport Kernel Embedding Method with G...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Yuan, Weitao Bian, Yuren Wang, Shengbei Unoki, Masashi Wang, Wenwu Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems China Japan Advanced Institute of Science and Technology Japan University of Surrey Centre for Vision Speech and Signal Processing Guildford United Kingdom

ISBN: (纸本)9781728163277

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be well reflected on its latent geometric structure of the feature distribution, a natural way to address SVS/SI is to extract the geometry-aware and distribution-related features of the target signal. To do this, this work introduces the concept of optimal transport (OT) to SVS/SI and proposes an improved optimal transport kernel embedding (iOTKE) to extract the target-distribution-related features. The iOTKE learns an OT from the input signal to the target signal on the basis of a reference set learned from all training data. Thus it can maintain the feature diversity and preserve the latent geometric structure of the distribution for the target signal. To further improve the feature selection ability, we extend the proposed iOTKE to a gated version, i.e., gated iOTKE (G-iOTKE), by incorporating a lightweight gating mechanism. The gating mechanism controls effective information flow and enables the proposed method to select important features for a specific input signal. We evaluated the proposed G-iOTKE on SVS/SI. Experimental results showed that the proposed method provided better results than other models. © 2023 IEEE.

关键词： Embeddings

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