2022 IEEE International Conference on Signal Processing and Communications (SPCOM)最新文献_第8页

A Hierarchical Approach for Decoding Human Reach-and-Grasp Activities based on EEG Signals 一种基于脑电图信号的人类伸手抓握活动分层解码方法

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840794

Bhagyasree Kanuparthi, A. Turlapaty

引用次数: 0

Glottal instants extraction from speech signal using Deep Feature Loss 基于深度特征损失的语音信号声门瞬时信号提取

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840808

Supritha M. Shetty, Suraj Durgesht, K. Deepak

{"title":"Glottal instants extraction from speech signal using Deep Feature Loss","authors":"Supritha M. Shetty, Suraj Durgesht, K. Deepak","doi":"10.1109/SPCOM55316.2022.9840808","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840808","url":null,"abstract":"Electroglottograph (EGG) is a device used to measure the conductance between the vocal folds. The analysis of EGG signal has many applications in the literature such as speech-to-text synthesis, voice disorder analysis, emotion recognition, speaker verification, etc. Therefore, the EGG device is essential to record the vocal folds activity. Alternatively, a new method is proposed in this work to synthesize the EGG waveform from speech signal using a context aggregation convolutional neural network. The synthesis network is trained by accounting the deep feature losses obtained by comparing it with another network called the EGG classification network. The synthesized EGG signal needs to be characterized. During the voiced speech production, the instants at which the vocal folds attain complete closure are called glottal closure instants (GCIs). Likewise, the opening instants are called glottal opening instants (GOIs). Such instants are reliably measured using the EGG signal. The performance of the proposed method is compared with other state-of-the-art techniques. The CMU-Arctic database has a parallel corpus of speech and EGG signal recorded simultaneously. This database is used for training the synthesis network and for comparison purposes. It is found that the performance of extracting glottal instants from synthesized EGG signals is comparable to other methods.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125330721","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

Increasing Transferability by Imposing Linearity and Perturbation in Intermediate Layer with Diverse Input Patterns 通过在不同输入模式的中间层施加线性和扰动来增加可转移性

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840512

Meet Shah, Srimanta Mandal, Shruti Bhilare, Avik Hati Dhirubhai

{"title":"Increasing Transferability by Imposing Linearity and Perturbation in Intermediate Layer with Diverse Input Patterns","authors":"Meet Shah, Srimanta Mandal, Shruti Bhilare, Avik Hati Dhirubhai","doi":"10.1109/SPCOM55316.2022.9840512","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840512","url":null,"abstract":"Despite high prediction accuracy, deep networks are vulnerable to adversarial attacks, designed by inducing human-indiscernible perturbations to clean images. Hence, adversarial samples can mislead already trained deep networks. The process of generating adversarial examples can assist us in investigating the robustness of different models. Many developed adversarial attacks often fail under challenging black-box settings. Hence, it is required to improve transferability of adversarial attacks to an unknown model. In this aspect, we propose to increase the rate of transferability by inducing linearity in a few intermediate layers of architecture. The proposed design does not disturb the original architecture much. The design focuses on significance of intermediate layers in generating feature maps suitable for a task. By analyzing the intermediate feature maps of architecture, a particular layer can be more perturbed to improve the transferability. The performance is further enhanced by considering diverse input patterns. Experimental results demonstrate the success in increasing the transferability of our proposition.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124562299","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

Temporal Surgical Gesture Segmentation and Classification in Multi-gesture Robotic Surgery using Fine-tuned features and Calibrated MS-TCN 基于微调特征和校准MS-TCN的多手势机器人手术时间外科手势分割与分类

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840779

Snigdha Agarwal, Chakka Sai Pradeep, N. Sinha

{"title":"Temporal Surgical Gesture Segmentation and Classification in Multi-gesture Robotic Surgery using Fine-tuned features and Calibrated MS-TCN","authors":"Snigdha Agarwal, Chakka Sai Pradeep, N. Sinha","doi":"10.1109/SPCOM55316.2022.9840779","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840779","url":null,"abstract":"Temporal Gesture Segmentation is an active research problem for many applications such as surgical skill assessment, surgery training, robotic training. In this paper, we propose a novel method for Gesture Segmentation on untrimmed surgical videos of the challenging JIGSAWS dataset by using a two-step methodology. We train and evaluate our method on 39 videos of the Suturing task which has 10 gestures. The length of gestures ranges from 1 second to 75 seconds and full video length varies from 1 minute to 5 minutes. In step one, we extract encoded frame-wise spatio-temporal features on full temporal resolution of the untrimmed videos. In step two, we use these extracted features to identify gesture segments for temporal segmentation and classification. To extract high-quality features from the surgical videos, we also pre-train gesture classification models using transfer learning on the JIGSAWS dataset using two state-of-the-art pretrained backbone architectures. For segmentation, we propose an improved calibrated MS-TCN (CMS-TCN) by introducing a smoothed focal loss as loss function which helps in regularizing our TCN to avoid making over-confident decisions. We achieve a frame-wise accuracy of 89.8% and an Edit Distance score of 91.5%, an improvement of 2.2% from previous works. We also propose a novel evaluation metric that normalizes the effect of correctly classifying the frames of larger segments versus smaller segments in a single score.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132781519","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

Computer-aided Cataract Grading Under Adversarial Environment 敌对环境下的计算机辅助白内障分级

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840821

T. Pratap, Priyanka Kokil

引用次数: 1

A unified neural MRA architecture combining wavelet CNN and wavelet pooling for texture classification 结合小波CNN和小波池的纹理分类统一神经MRA架构

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840760

K. K. Tarafdar, Q. Saifee, V. Gadre

引用次数: 0

Morse Wavelet Features for Pop Noise Detection 莫尔斯小波特征的流行噪声检测

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840840

Priyanka Gupta, Piyushkumar K. Chodingala, H. Patil

{"title":"Morse Wavelet Features for Pop Noise Detection","authors":"Priyanka Gupta, Piyushkumar K. Chodingala, H. Patil","doi":"10.1109/SPCOM55316.2022.9840840","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840840","url":null,"abstract":"Spoofed Speech Detection (SSD) problem has been an important problem, especially for Automatic Speaker Verification (ASV) systems. However, the techniques used for designing countermeasure systems for SSD task are attack-specific, and therefore the solutions are far from a generalized SSD system, which can detect any type of spoofed speech. On the other hand, Voice Liveness Detection (VLD) systems rely on the characteristics of live speech (i.e., pop noise) to detect whether an utterance is live or not. Given that the attacker has the freedom to mount any type of attack, VLD systems play a crucial role in defending against spoofing attacks, irrespective of the type of spoof used by the attacker. To that effect, we propose Generalized Morse Wavelet (GMW)-based features for VLD, with Convolutional Neural Network (CNN) as the classifier at the back-end. In this context, we use pop noise as a discriminative acoustic cue to detect live speech. Pop noise is present in live speech signals at low frequencies (typically $leq 40$ Hz), caused by human breath reaching at the closely-placed microphone. We show that for $gamma =3$, the Morse wavelet has the highest concentration of information denoted by the least area of the Heisenberg’s box. Hence, we take $gamma =3$ for our experiments on Morse wavelets. We compare the performance of our system with Short-Time Fourier Transform (STFT)-Support Vector Machine (SVM)-based original baseline, and other existing systems, such as Constant Q-Transform (CQT)-SVM, STFT-CNN, and bump wavelet-CNN. With overall accuracy of 86.90% on evaluation set, our proposed system significantly outperforms STFT-SVM-based original baseline, CQT-SVM, STFT-CNN, and bump wavelet-CNN by an absolute margin of 18.97 %, 8. 02%, 15. 09%, and 12. 21%, respectively. Finally, we have also analyzed the effect of various phoneme types on VLD system performance.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124847403","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

Binary Intelligent Reflecting Surfaces Assisted OFDM Systems 二元智能反射面辅助OFDM系统

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840791

L. Yashvanth, C. Murthy, B. Deepak

引用次数: 0

C-Band Iris Coupled Cavity Bandpass Filter c波段虹膜耦合腔带通滤波器

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840777

Shashank Soi, Sudheer Kumar Singh, Rajendra Singh, Ashok Kumar

引用次数: 0

Hilbert Vibration Decomposition of Seismocardiogram for HR and HRV Estimation 用于HR和HRV估计的地震心电图希尔伯特振动分解

2022 IEEE International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840838

Moirangthem James Singh, L. Sharma, S. Dandapat

{"title":"Hilbert Vibration Decomposition of Seismocardiogram for HR and HRV Estimation","authors":"Moirangthem James Singh, L. Sharma, S. Dandapat","doi":"10.1109/SPCOM55316.2022.9840838","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840838","url":null,"abstract":"This paper presents a new time-varying decomposition method based on the Hilbert Vibration Decomposition (HVD) for estimating heart rate from a Seismocardiogram (SCG). The heart rate (HR) estimation method consists of signal decomposition using the HVD algorithm, heart rate envelope generation, and peak detection from the smooth envelope for beat-to-beat interval calculation. We derived the heart rate variability (HRV) metrics from the interbeat intervals. The method doesn’t require a reference ECG signal. The same signals are also subjected to Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) methods. To compare these three decomposition methods, the CEBS database from Physionet Archive was used for testing and validation. The results show better accuracy in beat-to-beat interval estimation using the HVD method than others, and HRV metrics are accurately derived using our methodology. The performance results demonstrate that the standard ECG-derived heartbeats and HRV metrics and SCG-derived heartbeats and HRV metrics are comparable for healthy subjects.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115669859","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