ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第9页

Progressive Co-Teaching for Ambiguous Speech Emotion Recognition 模糊语音情感识别的渐进式联合教学

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414494

Yifei Yin, Yu Gu, Longshan Yao, Ying Zhou, Xuefeng Liang, He Zhang

引用次数: 3

Identifying Spammers to Boost Crowdsourced Classification 识别垃圾邮件发送者以促进众包分类

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414242

Panagiotis A. Traganitis, G. Giannakis

引用次数: 2

Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning 基于自适应非锐化和深度学习的急性淋巴细胞白血病检测

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414362

A. Genovese, M. S. Hosseini, V. Piuri, K. Plataniotis, F. Scotti

{"title":"Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning","authors":"A. Genovese, M. S. Hosseini, V. Piuri, K. Plataniotis, F. Scotti","doi":"10.1109/ICASSP39728.2021.9414362","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414362","url":null,"abstract":"Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging fields, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has specifically analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classification. To address this issue, in this paper we propose the first machine learning-based approach able to enhance blood sample images by an adaptive unsharpening method. The method uses image processing techniques and DL to normalize the radius of the cell, estimate the focus quality, adaptively improve the sharpness of the images, and then perform the classification. We evaluated the methodology on a public database of ALL images, considering several state-of-the-art CNNs to perform the classification, with results showing the validity of the proposed approach. For a complete reproducibility of the work, the source code is available at: http://iebil.di.unimi.it/cnnALL/index.htm.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130451136","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: 27

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414668

Jian Wang, Zhichao Zhang, Dongmei Huang, Wei Song, Quanmiao Wei, Xinyue Li

{"title":"A Ranked Similarity Loss Function with pair Weighting for Deep Metric Learning","authors":"Jian Wang, Zhichao Zhang, Dongmei Huang, Wei Song, Quanmiao Wei, Xinyue Li","doi":"10.1109/ICASSP39728.2021.9414668","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414668","url":null,"abstract":"Metric learning is a widely-used method for image retrieval. The object of metric learning is to limit the distance between similar samples and increase the distance between samples of different classes through learning. Many studies tend to pay more attention to keep the distance between positive and negative samples, but ignore the distance between different classes of negative samples. In fact, query samples should be separated from negative samples of different classes by different distances. To address these problems, we propose to build a ranked similarity loss function with pair weighting (dubbed RMS loss). The proposed RMS loss can keep a distance between samples of different classes by weighting the negative samples according to the sorting order. Meanwhile, it further widens the distance between positive and negative samples by different processing of similarity of positive pairs and negative pairs. The effectiveness of our method is evaluated by extensive experiments on four public datasets and compared with state-of-the-art methods. The results show the proposed method obtains new performance on four public datasets, e.g., reaching 67.4% on CUB200 at Recall@1.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134408805","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

Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting a Noise Dictionary 基于噪声字典的有监督声系统辨识的噪声鲁棒自适应控制

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414180

Thomas Haubner, Andreas Brendel, Mohamed Elminshawi, Walter Kellermann

{"title":"Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting a Noise Dictionary","authors":"Thomas Haubner, Andreas Brendel, Mohamed Elminshawi, Walter Kellermann","doi":"10.1109/ICASSP39728.2021.9414180","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414180","url":null,"abstract":"We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noisy observations by a linear Gaussian Discrete Fourier Transform-domain state space model whose parameters are estimated by an online generalized Expectation-Maximization algorithm. Unlike all other state-of-the-art approaches we suggest to model the covariance matrix of the observation probability density function by a dictionary model. We propose to learn the noise dictionary from training data, which can be gathered either offline or online whenever the system is not excited, while we infer the activations continuously. The proposed algorithm represents a novel machine-learning-based approach to noise-robust adaptation control which allows for faster convergence in applications characterized by high-level and non-stationary interfering noise signals and abrupt system changes.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902493","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: 10

Task Aware Multi-Task Learning for Speech to Text Tasks 语音到文本任务的任务感知多任务学习

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414703

S. Indurthi, Mohd Abbas Zaidi, Nikhil Kumar Lakumarapu, Beomseok Lee, HyoJung Han, Seokchan Ahn, Sangha Kim, Chanwoo Kim, Inchul Hwang

引用次数: 18

Gating Feature Dense Network for Single Anisotropic Mr Image Super-Resolution 单幅各向异性磁流变图像超分辨率的门控特征密集网络

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414646

W. He, Yangjinan Hu, Lulu Wang, Zhongshi He, Jinglong Du

引用次数: 0

Generalized Thinned Coprime Array for DOA Estimation 用于DOA估计的广义稀疏协素阵列

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414146

Junpeng Shi, Yongxiang Liu, Fang-qing Wen, Zhen Liu, Panhe Hu, Zhenghui Gong

引用次数: 3

Unsupervised Stacked Capsule Autoencoder for Hyperspectral Image Classification 用于高光谱图像分类的无监督堆叠胶囊自编码器

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413664

Erting Pan, Yong Ma, Xiaoguang Mei, Fan Fan, Jiayi Ma

{"title":"Unsupervised Stacked Capsule Autoencoder for Hyperspectral Image Classification","authors":"Erting Pan, Yong Ma, Xiaoguang Mei, Fan Fan, Jiayi Ma","doi":"10.1109/ICASSP39728.2021.9413664","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9413664","url":null,"abstract":"Since CapsNet [1] shattered all previous records of algorithms for image recognition, the capsule's conception has attracted bright attention. It interprets an object by the geometrical arrangement of parts. We think it can be transferred to hyperspectral images. In a hyperspectral data cube, each pixel spectrum can be regarded as a continuous curve representing its inherent properties. In the spatial domain, there are various spatial distributions in different positionsand there is usually a specific structural relationship between adjacently distributed categories. Based on HSI data's aforementioned structural characteristics, combined with the stacked capsule autoencoder, we propose our model to achieve an unsupervised HSI classification. In our model, the ConvLSTM is employed to discover part capsules of HSI, and we utilize Set Transformer to encode relations among all parts and indicate object capsules. The decoders of both phases use Gaussian mixture models to reconstruct specific information. Experimental results of the Pavia Center dataset show the exceptional of our model.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127562213","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: 4

A New Tubular Structure Tracking Algorithm Based On Curvature-Penalized Perceptual Grouping 一种基于曲率惩罚感知分组的管状结构跟踪新算法

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414114

Li Liu, Da Chen, Minglei Shu, H. Shu, L. Cohen

引用次数: 2