ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第3页

Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs 基于图上随机抽样和一致性的异常节点揭示

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414953

V. N. Ioannidis, Dimitris Berberidis, G. Giannakis

引用次数: 6

Structured Support Exploration for Multilayer Sparse Matrix Factorization 多层稀疏矩阵分解的结构化支持探索

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414238

Quoc-Tung Le, R. Gribonval

引用次数: 7

End-to-End Multilingual Automatic Speech Recognition for Less-Resourced Languages: The Case of Four Ethiopian Languages 端到端多语言自动语音识别资源较少的语言:四种埃塞俄比亚语言的情况

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9415020

S. Abate, Martha Yifiru Tachbelie, T. Schultz

{"title":"End-to-End Multilingual Automatic Speech Recognition for Less-Resourced Languages: The Case of Four Ethiopian Languages","authors":"S. Abate, Martha Yifiru Tachbelie, T. Schultz","doi":"10.1109/ICASSP39728.2021.9415020","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9415020","url":null,"abstract":"The End-to-End (E2E) approach, which maps a sequence of input features into a sequence of graphemes or words, to Automatic Speech Recognition (ASR) is a hot research agenda. It is interesting for less-resourced languages since it avoids the use of pronunciation dictionary, which is one of the major components in the traditional ASR systems. However, like any deep neural network (DNN) approaches, E2E is data greedy. This makes the application of E2E to less-resourced languages questionable. However, using data from other languages in a multilingual (ML) setup is being applied to solve the problem of data scarcity. We have, therefore, conducted ML E2E ASR experiments for four less-resourced Ethiopian languages using different language and acoustic modelling units. The results of our experiments show that relative Word Error Rate (WER) reductions (over the monolingual E2E systems) of up to 29.83% can be achieved by just using data of two related languages in E2E ASR system training. Moreover, we have also noticed that the use of data from less related languages also leads to E2E ASR performance improvement over the use of monolingual data.","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":"115019358","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: 5

Exploiting Non-Negative Matrix Factorization for Binaural Sound Localization in the Presence of Directional Interference 利用非负矩阵分解法进行定向干扰下双耳声音定位

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414233

Ingvi Örnolfsson, T. Dau, Ning Ma, T. May

{"title":"Exploiting Non-Negative Matrix Factorization for Binaural Sound Localization in the Presence of Directional Interference","authors":"Ingvi Örnolfsson, T. Dau, Ning Ma, T. May","doi":"10.1109/ICASSP39728.2021.9414233","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414233","url":null,"abstract":"This study presents a novel solution to the problem of binaural localization of a speaker in the presence of interfering directional noise and reverberation. Using a state-of-the-art binaural localization algorithm based on a deep neural network (DNN), we propose adding a source separation stage based on non-negative matrix factorization (NMF) to improve the localization performance in conditions with interfering sources. The separation stage is coupled with the localization stage and is optimized with respect to a broad range of different acoustic conditions, emphasizing a robust and generalizable solution. The machine listening system is shown to greatly benefit from the NMF-based separation stage at low target-to-masker ratios (TMRs) for a variety of noise types, especially for non-stationary noise. It is also demonstrated that training the NMF algorithm on anechoic speech provides better performance than using reverberant speech, and that optimizing the source separation stage using a localization metric rather than a source separation metric substantially increases the system performance.","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":"115222588","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: 3

Prediction of Egfr Mutation Status in Lung Adenocarcinoma Using Multi-Source Feature Representations 利用多源特征表征预测肺腺癌中Egfr突变状态

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414064

Jianhong Cheng, Jin Liu, M. Jiang, H. Yue, Lin Wu, Jianxin Wang

{"title":"Prediction of Egfr Mutation Status in Lung Adenocarcinoma Using Multi-Source Feature Representations","authors":"Jianhong Cheng, Jin Liu, M. Jiang, H. Yue, Lin Wu, Jianxin Wang","doi":"10.1109/ICASSP39728.2021.9414064","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414064","url":null,"abstract":"Epidermal growth factor receptor (EGFR) genotyping is essential to treatment guidelines for the use of tyrosine kinase inhibitors in lung adenocarcinoma. However, accurate and noninvasive methods to detect the EGFR gene are ongoing challenges. In this study, we propose a hybrid framework, namely HC-DLR, to noninvasively predict EGFR mutation status by fusing multi-source features including low-level handcrafted radiomics (HCR) features, high-level deep learning-based radiomics (DLR) features, and demographics features. The HCR features first are selected from massive handcrafted features extracted from CT images. The DLR features are also extracted from CT images using the pre-trained 3D DenseNet. Then, multi-source feature representations are refined and fused to build an HC-DLR model for improving the predictive performance of EGFR mutations. The proposed method is evaluated on a newly collected dataset with 670 patients. Experimental results show that the HC-DLR model achieves an encouraging predictive performance with an AUC of 0.76, an accuracy of 72.47%, and an F1-score of 71.35%, which may have potential clinical value for predicting EGFR mutations in lung adenocarcinoma.","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":"115577867","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: 3

Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation 基于联合优化和盲去噪的低延迟在线盲信源分离

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413700

Tetsuya Ueda, T. Nakatani, Rintaro Ikeshita, K. Kinoshita, S. Araki, S. Makino

引用次数: 7

Compressing Deep Neural Networks for Efficient Speech Enhancement 压缩深度神经网络用于高效语音增强

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413536

Ke Tan, Deliang Wang

引用次数: 13

Nested Error Map Generation Network for No-Reference Image Quality Assessment 无参考图像质量评估的嵌套误差图生成网络

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413489

Junming Chen, Haiqiang Wang, Ge Li, Shan Liu

引用次数: 2

Blind Extraction of Moving Audio Source in a Challenging Environment Supported by Speaker Identification Via X-Vectors 基于x向量说话人识别的环境下运动声源的盲提取

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414331

J. Málek, Jakub Janský, Tomás Kounovský, Zbyněk Koldovský, J. Zdánský

{"title":"Blind Extraction of Moving Audio Source in a Challenging Environment Supported by Speaker Identification Via X-Vectors","authors":"J. Málek, Jakub Janský, Tomás Kounovský, Zbyněk Koldovský, J. Zdánský","doi":"10.1109/ICASSP39728.2021.9414331","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414331","url":null,"abstract":"We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. The blind part of the method is based on independent vector extraction (IVE) and uses the recently proposed constant separating vector (CSV) mixing model. This model allows for changes of mixing parameters within the processed interval of the mixture, which potentially leads to higher accuracy of SOI estimation. The supervised part of the method concerns a pilot signal, which is related to the SOI and ensures the convergence of the blind method towards the SOI. The pilot is based on robust detection of frames where SOI is dominant via speaker embeddings called X-vectors. Robustness of the detection is achieved through augmentation of the data for the supervised training of the X-vectors. The pilot-supported extraction yields significantly better performance compared to its unsupervised counterpart identifying SOI solely using the initialization.","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":"116563347","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: 6

A Scale Invariant Measure of Flatness for Deep Network Minima 深度网络最小值平坦度的尺度不变度量

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413771

Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, D. Phan, S. Chin, T. Tran

{"title":"A Scale Invariant Measure of Flatness for Deep Network Minima","authors":"Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, D. Phan, S. Chin, T. Tran","doi":"10.1109/ICASSP39728.2021.9413771","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9413771","url":null,"abstract":"It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. However, for deep networks with positively homogeneous activations, most measures of flatness are not invariant to rescaling of the network parameters. This means that the measure of flatness can be made as small or as large as possible through rescaling, rendering the quantitative measures meaningless. In this paper we show that for deep networks with positively homogenous activations, these rescalings constitute equivalence relations, and that these equivalence relations induce a quotient manifold structure in the parameter space. Using an appropriate Riemannian metric, we propose a Hessian-based measure for flatness that is invariant to rescaling and perform simulations to empirically verify our claim. Finally we perform experiments to verify that our flatness measure correlates with generalization by using minibatch stochastic gradient descent with different batch sizes to find deep network minima with different generalization properties.","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":"122428929","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