ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第9页

Wishart Localization Prior On Spatial Covariance Matrix In Ambisonic Source Separation Using Non-Negative Tensor Factorization 非负张量分解双声源分离中空间协方差矩阵的Wishart定位先验

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/ICASSP43922.2022.9746222

Mateusz Guzik, K. Kowalczyk

{"title":"Wishart Localization Prior On Spatial Covariance Matrix In Ambisonic Source Separation Using Non-Negative Tensor Factorization","authors":"Mateusz Guzik, K. Kowalczyk","doi":"10.1109/ICASSP43922.2022.9746222","DOIUrl":"https://doi.org/10.1109/ICASSP43922.2022.9746222","url":null,"abstract":"This paper presents an extension of the existing Non-negative Tensor Factorization (NTF) based method for sound source separation under reverberant conditions, formulated for Ambisonic microphone mixture signals. In particular, we address the problem of optimal exploitation of the prior knowledge concerning the source localization, through the formulation of a suitable Maximum a Posteriori (MAP) framework. Within the presented approach, the magnitude spectrograms are modelled by the NTF and the individual source Spatial Covariance Matrices (SCM) are approximated as a sum of anechoic Spherical Harmonic (SH) components, weighted with the so-called spatial selector. We constrain the SCM using the Wishart distribution, which leads to a new posterior probability and in turn to the derivation of the extended update rules. The proposed solution avoids the issues encountered in the original method, related to the empirical binary initialization strategy for the spatial selector weights, which due to multiplicative update rules may result in sound coming from certain directions not being taken into account. The proposed method is evaluated against the original algorithm and another recently proposed Expectation Maximization (EM) algorithm that also incorporates a spatial localization prior, showing improved separation performance in experiments with first-order Ambisonic recordings of musical instruments and speech utterances.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902049","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

Patch Steganalysis: A Sampling Based Defense Against Adversarial Steganography 补丁隐写分析:基于采样的对抗性隐写防御

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747638

Chuan Qin, Na Zhao, Weiming Zhang, Nenghai Yu

{"title":"Patch Steganalysis: A Sampling Based Defense Against Adversarial Steganography","authors":"Chuan Qin, Na Zhao, Weiming Zhang, Nenghai Yu","doi":"10.1109/icassp43922.2022.9747638","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9747638","url":null,"abstract":"In recent years, the classification accuracy of CNN (convolutional neural network) steganalyzers has rapidly improved. However, as general CNN classifiers will misclassify adversarial samples, CNN steganalyzers can hardly detect adversarial steganography, which combines adversarial samples and steganography. Adversarial training and preprocessing are two effective methods to defend against adversarial samples. But literature shows adversarial training is ineffective for adversarial steganography. Steganographic modifications will also be destroyed by preprocessing, which aims to wipe out adversarial perturbations. In this paper, we propose a novel sampling based defense method for steganalysis. Specifically, by sampling image patches, CNN steganalyzers can bypass the sparse adversarial perturbations and extract effective features. Additionally, by calculating statistical vectors and regrouping deep features, the impact on the classification accuracy of common samples is effectively compressed. The experiments show that the proposed method can significantly improve the robustness against adversarial steganography without adversarial training.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131834370","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

Designing a QAM Signal Detector for Massive Mimo Systems via PS-ADMM Approach 基于PS-ADMM方法的大规模Mimo系统QAM信号检测器设计

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747281

Quan Zhang, Xuyang Zhao, Jiangtao Wang, Yongchao Wang

引用次数: 1

Point-Mass Filter with Decomposition of Transient Density 瞬态密度分解的点质量滤波器

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747607

P. Tichavský, O. Straka, J. Duník

引用次数: 4

A Multitask Learning Framework for Speaker Change Detection with Content Information from Unsupervised Speech Decomposition 基于无监督语音分解内容信息的说话人变化检测多任务学习框架

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746116

Hang Su, Danyang Zhao, Long Dang, Minglei Li, Xixin Wu, Xunying Liu, Helen M. Meng

{"title":"A Multitask Learning Framework for Speaker Change Detection with Content Information from Unsupervised Speech Decomposition","authors":"Hang Su, Danyang Zhao, Long Dang, Minglei Li, Xixin Wu, Xunying Liu, Helen M. Meng","doi":"10.1109/icassp43922.2022.9746116","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746116","url":null,"abstract":"Speaker Change Detection (SCD) is a task of determining the time boundaries between speech segments of different speakers. SCD system can be applied to many tasks, such as speaker diarization, speaker tracking, and transcribing audio with multiple speakers. Recent advancements in deep learning lead to approaches that can directly detect the speaker change points from audio data at the frame-level based on neural network models. These approaches may be further improved by utilizing speaker information in the training data, and utilizing content information extracted in an unsupervised manner. This work proposes a novel framework for the SCD task, which utilizes a multitask learning architecture to leverage speaker information during the training stage, and adds the content information extracted from an unsupervised speech decomposition model to help detect the speaker change points. Experiment results show that the architecture of multitask learning with speaker information can improve the performance of SCD, and adding content information extracted from unsupervised speech decomposition model can further improve the performance. To the best of our knowledge, this work outperforms the state-of-the-art SCD results [1] on the AMI dataset.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130727519","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

A Two-Stage Contrastive Learning Framework For Imbalanced Aerial Scene Recognition 不平衡航拍场景识别的两阶段对比学习框架

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746248

Lexing Huang, Senlin Cai, Yihong Zhuang, Changxing Jing, Yue Huang, Xiaotong Tu, Xinghao Ding

{"title":"A Two-Stage Contrastive Learning Framework For Imbalanced Aerial Scene Recognition","authors":"Lexing Huang, Senlin Cai, Yihong Zhuang, Changxing Jing, Yue Huang, Xiaotong Tu, Xinghao Ding","doi":"10.1109/icassp43922.2022.9746248","DOIUrl":"https://doi.org/10.1109/icassp43922.2022.9746248","url":null,"abstract":"In real-world scenarios, aerial image datasets are generally class imbalanced, where the majority classes have rich samples, while the minority classes only have a few samples. Such class imbalanced datasets bring great challenges to aerial scene recognition. In this paper, we explore a novel two-stage contrastive learning framework, which aims to take care of representation learning and classifier learning, thereby boosting aerial scene recognition. Specifically, in the representation learning stage, we design a data augmentation policy to improve the potential of contrastive learning according to the characteristics of aerial images. And we employ supervised contrastive learning to learn the association between aerial images of the same scene. In the classification learning stage, we fix the encoder to maintain good representation and use the re-balancing strategy to train a less biased classifier. A variety of experimental results on the imbalanced aerial image datasets show the advantages of the proposed two-stage contrastive learning framework for the imbalanced aerial scene recognition.","PeriodicalId":272439,"journal":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"87 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130922722","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

Convex Clustering for Autocorrelated Time Series 自相关时间序列的凸聚类

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747143

Max Revay, V. Solo

引用次数: 0

Domain Adaptation for Speaker Recognition in Singing and Spoken Voice 歌唱和口语中说话人识别的域自适应

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9746111

Anurag Chowdhury, Austin Cozzo, A. Ross

引用次数: 2

A Non-Convex Proximal Approach for Centroid-Based Classification 一种基于质心的非凸近端分类方法

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/ICASSP43922.2022.9747071

Mewe-Hezoudah Kahanam, L. Brusquet, Ségolène Martin, J. Pesquet

引用次数: 0

TCRNet: Make Transformer, CNN and RNN Complement Each Other TCRNet:使Transformer、CNN和RNN互补

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2022-05-23 DOI: 10.1109/icassp43922.2022.9747716

Xinxin Shan, Tai Ma, Anqi Gu, Haibin Cai, Ying Wen

引用次数: 0