2022 30th European Signal Processing Conference (EUSIPCO)最新文献_第6页

Hypernetworks for Sound event Detection: a Proof-of-Concept 用于声音事件检测的超网络:概念验证

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909716

Shubhr Singh, Huy Phan, Emmanouil Benetos

{"title":"Hypernetworks for Sound event Detection: a Proof-of-Concept","authors":"Shubhr Singh, Huy Phan, Emmanouil Benetos","doi":"10.23919/eusipco55093.2022.9909716","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909716","url":null,"abstract":"Polyphonic sound event detection (SED) involves the pre-diction of sound events present in an audio recording along with their onset and offset times. Recently, Deep Neural Net-works, specifically convolutional recurrent neural networks (CRNN) have achieved impressive results for this task. The convolution part of the architecture is used to extract trans-lational invariant features from the input and the recurrent part learns the underlying temporal relationship between au-dio frames. Recent studies showed that the weight sharing paradigm of recurrent networks might be a hindering factor in certain kinds of time series data, specifically where there is a temporal conditional shift, i.e. the conditional distribution of a label changes across the temporal scale. This warrants a relevant question - is there a similar phenomenon in poly-phonic sound events due to dynamic polyphony level across the temporal axis? In this work, we explore this question and inquire if relaxed weight sharing improves performance of a CRNN for polyphonic SED. We propose to use hyper-networks to relax weight sharing in the recurrent part and show that the CRNN's performance is improved by ≈ 3% across two datasets, thus paving the way for further explo-ration of the existence of temporal conditional shift for poly-phonic SED.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Online Speaker Diarization using Vector Quantization with Alternative Codebooks 基于矢量量化的可选码本半监督在线说话人二分化

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909891

Mahmoud El-Hindi, Michael Muma, A. Zoubir

{"title":"Semi-Supervised Online Speaker Diarization using Vector Quantization with Alternative Codebooks","authors":"Mahmoud El-Hindi, Michael Muma, A. Zoubir","doi":"10.23919/eusipco55093.2022.9909891","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909891","url":null,"abstract":"Speaker diarization systems process audio files by labelling speech segments according to speakers' identities. Many speaker diarization systems work offline and are not suited for online applications. We present a semi-supervised, online, low-complexity system. While, in general, speaker diarization operates in an unsupervised manner, the presented system relies on the enrollment of the participating speakers in the conversation. The diarization system has two main novel aspects. The first one is a proposed online learning strategy that evaluates processed segments according to their usefulness for learning a speaker, i.e. update a speaker model with it. The segment is evaluated using two metrics to determine whether to use the segment to update the system. The second novel aspect is a proposed vector quantization approach that models the score not only depending on the target speaker codebook but also takes an alternative codebook into account. We also present an approach to compute the alternative codebook. Simulation results show that the proposed system outperforms a comparable system without the proposed online learning strategy and shows benefits, especially for short training lengths.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127203912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Level Semantic Segmentation Guided Feature Space Decoupling And Augmentation for Fine-Grained Ship Detection 基于跨层次语义分割的细粒度船舶检测特征空间解耦与增强

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909586

Zhengning Zhang, Lin Zhang, Yue Wang, P. Feng, Shaobo Liu, Jian Wang

引用次数: 0

An efficient clustering-based non-fiducial approach for ECG biometric recognition 一种有效的基于聚类的心电生物特征识别非基准方法

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909751

David Meltzer, D. Luengo

{"title":"An efficient clustering-based non-fiducial approach for ECG biometric recognition","authors":"David Meltzer, D. Luengo","doi":"10.23919/eusipco55093.2022.9909751","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909751","url":null,"abstract":"Recognition of individuals through different bio-metric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative traits have been proposed during the last two decades: ECG and EEG signals, iris or facial recognition, behavioral traits, etc. Several works have shown that ECG-based recognition is a feasible alternative for either stand-alone or multibiometric recognition systems. In this paper, we propose a novel, efficient and scalable clustering-based method for ECG biometric recognition. First of all, fixed length segments of the ECG are extracted without the need of computing any fiducial points. Unique traits for each subject are then obtained by computing the autocorrelation (AC) of each segment, followed by the discrete cosine transform (DCT) to compress the available information. Finally, hierarchical ag-glomerative clustering (HAC) is applied to generate the groups that will be used later on for classification. The combination of the DCT to reduce the feature dimensionality and the HAC to decrease the number of features required by the classifier results in an efficient method both from the memory storage and computational point of view. Furthermore, the proposed AC/DCT-HAC (ADH) approach is robust, since no fiducial points (which may be difficult to extract reliably) are required, scalable and attains a better performance than other approaches with higher storage/computational cost.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130553894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Based Online Learning for Joint Radar-Communication Systems Operating in Dynamic Interference 动态干扰下联合雷达-通信系统基于模型的在线学习

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909601

Petteri Pulkkinen, V. Koivunen

引用次数: 1

A Low-Complexity Double EP-Based DFE for Turbo Equalization 基于低复杂度双ep的Turbo均衡DFE

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909782

Congji Yin, Wenjiang Feng, Junbing Li, Guojun Li

引用次数: 0

The Cover Source Mismatch Problem in Deep-Learning Steganalysis 深度学习隐写分析中的覆盖源不匹配问题

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909553

Quentin Giboulot, Patrick Bas, R. Cogranne, Dirk Borghys

引用次数: 2

Deep Residual Learning Based Localization of Near-Field Sources in Unknown Spatially Colored Noise Fields 基于深度残差学习的未知空间有色噪声场近场源定位

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909877

Zhuoqian Jiang, J. Xin, Weiliang Zuo, Nanning Zheng, A. Sano

引用次数: 1

Ensemble Link Learning for Large State Space Multiple Access Communications 面向大状态空间多址通信的集成链路学习

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909958

Talha Bozkus, U. Mitra

引用次数: 1

A Neural Network Approach for Ultrasound Attenuation Coefficient Estimation 超声衰减系数估计的神经网络方法

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909948

J. Birdi, J. D’hooge, A. Bertrand

{"title":"A Neural Network Approach for Ultrasound Attenuation Coefficient Estimation","authors":"J. Birdi, J. D’hooge, A. Bertrand","doi":"10.23919/eusipco55093.2022.9909948","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909948","url":null,"abstract":"Quantitative ultrasound (QUS) imaging complements the standard B-mode images with a quantitative represen-tation of the target's acoustic properties. Attenuation coefficient is an important parameter characterizing these properties, with applications in medical diagnosis and tissue characterization. Traditional QUS methods use analytical models to estimate this coefficient from the acquired signal. Propagation effects, such as diffraction, which are difficult to model analytically are usually ignored, affecting their estimation accuracy. To tackle this issue, reference phantom measurements are commonly used. These are, however, time-consuming and may not always be feasible, limiting the existing approaches' practical applicability. To overcome these challenges, we leverage recent advances in the deep learning field and propose a neural network approach which takes the magnitude spectra of the backscattered ultrasound signal at different axial depths as the input and provides the target's attenuation coefficient as the output. For the presented proof-of-concept study, the network was trained on a simulated dataset, and learnt a proper model from the training data, thereby avoiding the need for an analytical model. The trained network was tested on both simulated and tissue-mimicking phantom datasets, demonstrating the capability of neural networks to provide accurate attenuation estimates from diffraction affected recordings without a reference phantom measurement.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126498877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1