2020 28th European Signal Processing Conference (EUSIPCO)最新文献_第10页

Salt Dome Detection Using Context-Aware Saliency 使用上下文感知显著性的盐丘检测

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287538

A. Lawal, Qadri Mayyala, A. Zerguine, Azeddine Beghdadi

引用次数: 0

Deep Learning Methods for Image Decomposition of Cervical Cells 基于深度学习的宫颈细胞图像分解方法

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287435

Tayebeh Lotfi Mahyari, R. Dansereau

{"title":"Deep Learning Methods for Image Decomposition of Cervical Cells","authors":"Tayebeh Lotfi Mahyari, R. Dansereau","doi":"10.23919/Eusipco47968.2020.9287435","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287435","url":null,"abstract":"One way to solve under-determined image decomposition is to use statistical information about the type of data to be decomposed. This information can be obtained by a deep learning where convolutional neural networks (CNN) are a subset recently used widely in image processing. In this paper, we have designed a two-stage CNN that takes cytology images of overlapped cervical cells and attempts to separate the cell images. In the first stage, we designed a CNN to segment overlapping cells. In the second stage, we designed a CNN that uses this segmentation and the original image to separate the regions. We implemented a CNN similar to U-Net for image segmentation and implemented a new network for the image separation. To train and test the proposed networks, we simulated 50000 cervical cell cytology images by overlaying individual images of real cervical cells using the Beer-Lambert law. Of these 50000 images, we used 49000 images for training and evaluated the method with 1000 test images. Results on these synthetic images give more than 97% segmentation accuracy and gives decomposition SSIM scores of more than 0.99 and PSNR score of more than 30 dB. Despite these positive results, the permutation problem that commonly effects signal separation occasionally occurred resulting in some cell structure mis-separation (for example, one cell given two nucleoli and the other given none). In addition, when the segmentation was poor from the first stage, the resulting separation was poor.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"52 1","pages":"1110-1114"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90549014","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

Investigation of Network Architecture for Single-Channel End-to-End Denoising 单通道端到端去噪网络体系结构研究

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287753

Takuya Hasumi, Tetsunori Kobayashi, Tetsuji Ogawa

引用次数: 0

Grad-LAM: Visualization of Deep Neural Networks for Unsupervised Learning 面向无监督学习的深度神经网络可视化

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287730

Alexander Bartler, Darius Hinderer, Bin Yang

引用次数: 3

Nonparametric Adaptive Value-at-Risk Quantification Based on the Multiscale Energy Distribution of Asset Returns 基于资产收益多尺度能量分布的非参数自适应风险价值量化

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287568

G. Tzagkarakis, F. Maurer, T. Dionysopoulos

{"title":"Nonparametric Adaptive Value-at-Risk Quantification Based on the Multiscale Energy Distribution of Asset Returns","authors":"G. Tzagkarakis, F. Maurer, T. Dionysopoulos","doi":"10.23919/Eusipco47968.2020.9287568","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287568","url":null,"abstract":"Quantifying risk is pivotal for every financial institution, with the temporal dimension being the key aspect for all the well-established risk measures. However, exploiting the frequency information conveyed by financial data, could yield improved insights about the inherent risk evolution in a joint time-frequency fashion. Nevertheless, the great majority of risk managers make no explicit distinction between the information captured by patterns of different frequency content, while relying on the full time-resolution data, regardless of the trading horizon. To address this problem, a novel value-at-risk (VaR) quantification method is proposed, which combines nonlinearly the time-evolving energy profile of returns series at multiple frequency scales, determined by the predefined trading horizon. Most importantly, our proposed method can be coupled with any quantile-based risk measure to enhance its performance. Experimental evaluation with real data reveals an increased robustness of our method in efficiently controlling under-/over-estimated VaR values.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"15 1","pages":"2393-2397"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82517821","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

Phase-coherent multichannel SDR - Sparse array beamforming 相参多通道SDR -稀疏阵列波束形成

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287664

M. Laakso, Robin Rajamäki, R. Wichman, V. Koivunen

引用次数: 4

Evaluation of Zero Frequency Filtering based Method for Multi-pitch Streaming of Concurrent Speech Signals 基于零频率滤波的多基音并发语音信号流处理方法评价

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287322

Mariem Bouafif Mansali, Tomas Bäckström, Z. Lachiri

引用次数: 0

Towards Finite-Time Consensus with Graph Convolutional Neural Networks 图卷积神经网络的有限时间一致性研究

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287610

Bianca Iancu, E. Isufi

引用次数: 3

One-Class based learning for Hybrid Spectrum Sensing in Cognitive Radio 基于一类学习的认知无线电混合频谱感知

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287326

M. Jaber, A. Nasser, N. Charara, A. Mansour, K. Yao

{"title":"One-Class based learning for Hybrid Spectrum Sensing in Cognitive Radio","authors":"M. Jaber, A. Nasser, N. Charara, A. Mansour, K. Yao","doi":"10.23919/Eusipco47968.2020.9287326","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287326","url":null,"abstract":"The main aim of the Spectrum Sensing (SS) in a Cognitive Radio system is to distinguish between the binary hypotheses H0: Primary User (PU) is absent and H1: PU is active. In this paper, Machine Learning (ML)-based hybrid Spectrum Sensing (SS) scheme is proposed. The scattering of the Test Statistics (TSs) of two detectors is used in the learning and prediction phases. As the SS decision is binary, the proposed scheme requires the learning of only the boundaries of H0-class in order to make a decision on the PU status: active or idle. Thus, a set of data generated under H0 hypothesis is used to train the detection system. Accordingly, unlike the existing ML-based schemes of the literature, no PU statistical parameters are required. In order to discriminate between H0-class and elsewhere, we used a one-class classification approach that is inspired by the Isolation Forest algorithm. Extensive simulations are done in order to investigate the efficiency of such hybrid SS and the impact of the novelty detection model parameters on the detection performance. Indeed, these simulations corroborate the efficiency of the proposed one-class learning of the hybrid SS system.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"1683-1686"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77083788","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: 2

CNN-based Note Onset Detection using Synthetic Data Augmentation 基于cnn的基于合成数据增强的音符起始检测

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287621

Mina Mounir, P. Karsmakers, T. Waterschoot

{"title":"CNN-based Note Onset Detection using Synthetic Data Augmentation","authors":"Mina Mounir, P. Karsmakers, T. Waterschoot","doi":"10.23919/Eusipco47968.2020.9287621","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287621","url":null,"abstract":"Detecting the onset of notes in music excerpts is a fundamental problem in many music signal processing tasks, including analysis, synthesis, and information retrieval. When addressing the note onset detection (NOD) problem using a data-driven methodology, a major challenge is the availability and quality of labeled datasets used for both model training/tuning and evaluation. As most of the available datasets are manually annotated, the amount of annotated music excerpts is limited and the annotation strategy and quality varies across data sets. To counter both problems, in this paper we propose to use semi-synthetic datasets where the music excerpts are mixes of isolated note recordings. The advantage resides in the annotations being automatically generated while mixing the notes, as isolated note onsets are straightforward to detect using a simple energy measure. A semi-synthetic dataset is used in this work for augmenting a real piano dataset when training a convolutional Neural Network (CNN) with three novel model training strategies. Training the CNN on a semi-synthetic dataset and retraining only the CNN classification layers on a real dataset results in higher average F1-score (F1) scores with lower variance.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"89 1","pages":"171-175"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74046757","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: 2