检索结果-内蒙古大学图书馆

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Deng Jinxin Qin Xizhong Yang Rong Lv Xiaoyi School of Information Science and Engineering Xinjiang University of China Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Information The Fifth Affiliated Hospital of Xinjiang Medical Urumqi China

Relational triple extraction from unstructured text, which is in form of (subject, relation, object), is crucial in information extraction and knowledge graph construction. However, most existing methods still suffer from some limitations, such as error propagation, redundancy of relation prediction, and poor generalization. To overcome these deficiencies, we propose a relation-first bidirectional cascade multi-task framework for relational triple extraction. This framework solves the relational redundancy problem by relation prediction, alleviates error propagation through a complementary bidirectional entity tagger, and uses multi-task learning to improve the generalization of the model. Concretely, we first use a relation detection component to predict potential relations in sentences and conduct the following entity pairs extraction based on specific relations. Then, we extract entity pairs from two paralleled directions. Meanwhile, entity recognition is used as an auxiliary task, improving generalization capability. Extensive experiments were conducted on the NYT and WebNLG datasets We evaluate our model on four public datasets to demonstrate the effectiveness of our proposed RFBiCF framework, even in the complex scenario of overlapping triples.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Speaker Recognition Method Based on Stable Learning

A Speaker Recognition Method Based on Stable Learning

引用

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

作者： Jian Zhang Jing Ma Xiaochen Guo Lin Li Liang He School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China School of Electronic Science and Engineering Xiamen University China Department of Electronic Engineering Tsinghua University China

With the development of deep learning, speaker recognition systems have shown increasingly better performance. The generalization ability of the models is also an important aspect of performance evaluation. Typically, a baseline system is used to compare against the improved models to demonstrate performance enhancements. However, we cannot determine the differences in learned voiceprint features between the improved models and the baseline system. This paper introduces an improved speaker recognition system based on the ECAPA-TDNN model. It utilizes stable learning to eliminate sample correlation and employs attribution analysis to compare the differences in voiceprint feature learning between the improved and baseline systems. Experimental results demonstrate that stable learning improves the model’s generalization performance and helps it learn better voiceprint features. The effectiveness and generalization capability of the proposed method are verified through experiments on the VoxCeleb, CNCeleb, and LibriSpeech datasets. This work is important for enhancing speaker recognition performance, analyzing differences in voiceprint feature learning, and promoting advancements in the field.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Boundary-Guided Fusion of Multi-Level Features Network for Camouflaged Object detection

Boundary-Guided Fusion of Multi-Level Features Network for C...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Songlin Li Zhe Li Boyuan Li Xiuhong Li Jiabao Sheng School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University HongKong China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged objects, exhibiting high similarity with their surroundings, pose a substantial challenge for both humans and machines to detect when concealed within the environment. Existing methods for camouflage object detection (COD) struggle in accurately segmenting the overall structure of camouflaged objects. To address this issue, we propose a novel boundary-guided fusion of multi-level features network (BGFM-Net) for COD. In contrast to existing boundary-guided methods, we pay more attention to addressing the significant imbalance in the pixel quantities between boundary and background features, allowing for a more comprehensive representation of boundary features. BGFM-Net primarily consists of a multi-scale aggregation module (MSAM), a boundary-guided feature module (BFM), and a cross-Level fusion module (CLFM). MSAM effectively integrates contextual semantics at different scales, achieving a powerful and efficient feature representation. BFM adeptly combines edge features while constraining interference from background features, guiding the learning of camouflaged object boundary representation. CLFM integrates multi-level features for predicting camouflaged objects while adaptively adjusting channel weights to emphasize important channels and diminish the impact of less relevant channels for the task. Extensive experiments on three benchmark camouflage datasets demonstrate that our BGFM-Net outperforms other state-of-the-art COD models.

关键词： Adaptation models Semantics Neural networks Object detection Interference Benchmark testing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Multi-View Speaker Embedding Learning for Enhanced Stability and Discriminability

Multi-View Speaker Embedding Learning for Enhanced Stability...

引用

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

作者： Liang He Zhihua Fang Zuoer Chen Minqiang Xu Ying Meng Penghao Wang 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 iFly Digital Technology Hefei China

Deep neural network models based on x-vector have become the most popular framework for speaker recognition, and the quality of speaker features (embeddings) is important for open-set tasks such as speaker verification and speaker diarization. Currently, the most popular loss function is based on margin penalty, however, it only considers enlarging the inter-class distance while neglecting to reduce the intra-class feature differences. Therefore, we propose a multi-view learning approach that divides the training process into two views from the speaker embedding level. The classification view focuses on distinguishing the discriminability of different speakers, while the clustering view focuses on shrinking the feature boundaries of the same speaker, making intra-class differences smaller. The combined effect of the two perspectives achieves large inter-class distance and small intra-class distances, resulting in the extraction of more discriminative and stable speaker embeddings. We test the performance of the method on both speaker verification and speaker diarization tasks, and the results demonstrate the effectiveness of our approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SMMA-Net: An Audio Clue-Based Target Speaker Extraction Network with Spectrogram Matching and Mutual Attention

SMMA-Net: An Audio Clue-Based Target Speaker Extraction Netw...

引用

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

作者： Ying Hu Haitao Xu Zhongcun Guo Hao Huang Liang He Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China School of Computer Science and Technology Xinjiang University Urumqi China HiSilicon Technologies Co. Limited Department of Electronic Engineering Tsinghua University Beijing China

We propose a deep neural network with spectrogram matching and mutual attention (SMMA-Net) for audio clue-based target speaker extraction (TSE). To effectively use the auxiliary speech, we proposed spectrogram matching (SM) strategy and mutual attention (MA) block. We conducted all experiments on the WSJ0-2mix-extr dataset. The ablation and comparison studies verified the effectiveness of SM strategy and MA block. The experimental results show that our proposed method outperforms the state-of-the-art methods by a sizable margin of 1.3 dB on the metric of scale-invariant signal-to-distortion ratio improvement. Additionally, SMMA-Net achieved that the performance of model for TSE task exceeds that for speaker separation task under the similar architecture. The main code will be available at https://***/Ht-Xu/SMMA-Net.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Disease Diagnosis Based on Teacher-Student Network and Raman Spectroscopy

SSRN

引用

SSRN 2023年

作者： Chen, Zishuo Tian, Xuecong Chen, Chen Lv, Xiaoyi Chen, Cheng College of Software Xinjiang University Xinjiang Urumqi830046 China College of Information Science and Engineering Xinjiang University Xinjian Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Urumqi830046 China

In the field of spectral analysis, the common Raman spectral feature selection model can extract features effectively, but it will change the original data *** teacher model assists the student model in distillation training by transferring the trained knowledge to the student *** this process, teacher models provide guidance and help student models better understand and master complex knowledge *** teacher-student network combined with Raman spectroscopy can perform feature selection on the premise of retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure *** the construction of teacher-student network, this study of diabetic nephropathy and primary Sjogren's syndrome to verify, select Isomap, t-SNE, LLE, MDS, SE five models as the teacher network, build MLP neural network as the student network, multi-layer perceptron classification, and according to the evaluation index accuracy, precision, recall, F1 index to select the best *** 50% cross-validation, the results showed that the accuracy of t-SNE and SE models in the diagnosis of diabetic nephropathy was up to 98.3%, which was 14.02% higher than that of existing *** the diagnosis of primary Sjogren's syndrome, the accuracy of other models except MDS was 100%, which was 10.48% higher than that of existing ***, Raman spectroscopy combined with teacher-student networks for disease diagnosis is an efficient and accurate protocol that has produced good experimental results in the diagnosis of diabetic nephropathy and Sjogren's *** study provides a new way of thinking for the diagnosis of other diseases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on Disease Diagnosis Based on Teacher-Student Network and Raman Spectroscopy

SSRN

引用

SSRN 2023年

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Adaptive Gaussian Regularization Constrained Sparse Subspace Clustering for Image Segmentation

Adaptive Gaussian Regularization Constrained Sparse Subspace...

引用

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

作者： Sensen Song Dayong Ren Zhenhong Jia Fei Shi School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Urumqi China College of Mathematics and System Science Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Sparse Subspace Clustering (SSC) is integral to image processing, drawing from spectral clustering foundations. However, prevalent methods, relying on an l 1 -norm constraint, fail to capture nuanced inter-region correlations, affecting segmentation efficacy. To remedy this, we introduce an Adaptive Gaussian Regularization Constrained SSC for enhanced image segmentation. This method begins with superpixel preprocessing to enrich local information. Given the Gaussian nature of the SSC’s sparse coefficient matrix, a Gaussian probability density function is infused as a regularization term, reinforcing regional image ties and facilitating similarity matrix creation. Using spectral clustering, we then define superpixel clusters leading to the final segmentation. When tested against the BSDS500 and SBD datasets and other leading algorithms, our model showcases marked improvements in natural image segmentation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Speech Synthesis Based on Prosodic Controllability

Research on Speech Synthesis Based on Prosodic Controllabili...

引用

Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Wenyu Gao Askar Hamdulla Xipeng Yang Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University of China Urumqi Xinjiang China Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang University of China Urumqi Xinjiang China Mobvoi AI Lab Suzhou China

In today's highly interactive human-computer world, speech synthesis is widely used in many scenarios, and the requirements for rhyme effects in speech synthesis technology are increasing, so rhyme-controllable models have become a research hotspot. Most current rhyme controllable models are achieved by using a separate neural network to generate reference features, but this approach requires the training of more complex neural network models and the availability of reference audio to achieve display rhyme control. This paper proposes a rhyme-controllable solution based on an end-to-end acoustic model to address the problem of models being unable to precisely control the tone at the word level. The proposed model includes a tone-controllable module, which obtains duration information through the MFA alignment tool and adjusts the tone of the words by using word-level pitch control values and duration information. The acoustic model in this paper is improved by introducing pitch control during the generation of acoustic features and generating more robust audio by combining it with the decoder. In addition, to adjust the overall tone of the audio, a fixed coefficient is multiplied to the pitch values of all frames. Furthermore, this paper also proposes a 48KHz ultra-high-definition audio model by increasing the spectral parameter dimensions and upsampling by a factor.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Speaker Recognition Based on Pre-Trained Model and Deep Clustering

Speaker Recognition Based on Pre-Trained Model and Deep Clus...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang He Zhida Song Shuanghong Liu Mengqi Niu Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence 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

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we propose a novel loss by integrating a deep clustering (DC) loss at the frame-level and a speaker recognition loss at the segment-level into a single network without additional data requirements and exhaustive computation. The DC loss implicitly generates soft pseudo-phoneme labels for each frame-level feature, which facilitates extracting more discriminant speaker representation by suppressing phonetic content information. We study the DC loss not only on the acoustic feature, but also on the features extracted by the pre-trained models, such as wav2vec 2.0, HuBERT and WavLM. Experimental results on the VoxCeleb dataset shows that the overall system performance based on the pre-trained model features are better than the one on the acoustic feature. The proposed loss is significantly effective for systems on the acoustic feature and has a marginal improvement for systems on the pre-trained model feature.

关键词： Training Costs Computational modeling System performance Phonetics Multimedia databases Feature extraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：