发布求助
文献互助 智能选刊 最新文献

2020 28th European Signal Processing Conference (EUSIPCO)最新文献

筛选
英文 中文
Analysis of Phonetic Dependence of Segmentation Errors in Speaker Diarization 说话人分词错误的语音依赖性分析
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287552
Simon W. McKnight, Aidan O. T. Hogg, P. Naylor
{"title":"Analysis of Phonetic Dependence of Segmentation Errors in Speaker Diarization","authors":"Simon W. McKnight, Aidan O. T. Hogg, P. Naylor","doi":"10.23919/Eusipco47968.2020.9287552","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287552","url":null,"abstract":"Evaluation of speaker segmentation and diarization normally makes use of forgiveness collars around ground truth speaker segment boundaries such that estimated speaker segment boundaries with such collars are considered completely correct. This paper shows that the popular recent approach of removing forgiveness collars from speaker diarization evaluation tools can unfairly penalize speaker diarization systems that correctly estimate speaker segment boundaries. The uncertainty in identifying the start and/or end of a particular phoneme means that the ground truth segmentation is not perfectly accurate, and even trained human listeners are unable to identify phoneme boundaries with full consistency. This research analyses the phoneme dependence of this uncertainty, and shows that it depends on (i) whether the phoneme being detected is at the start or end of an utterance and (ii) what the phoneme is, so that the use of a uniform forgiveness collar is inadequate. This analysis is expected to point the way towards more indicative and repeatable assessment of the performance of speaker diarization systems.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"20 1","pages":"381-385"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90884623","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
Orientation-Matched Multiple Modeling for RSSI-based Indoor Localization via BLE Sensors 基于rssi的BLE传感器室内定位方向匹配多重建模
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287489
M. Atashi, Parvin Malekzadeh, Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, K. Plataniotis, Arash Mohammadi
{"title":"Orientation-Matched Multiple Modeling for RSSI-based Indoor Localization via BLE Sensors","authors":"M. Atashi, Parvin Malekzadeh, Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, K. Plataniotis, Arash Mohammadi","doi":"10.23919/Eusipco47968.2020.9287489","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287489","url":null,"abstract":"Internet of Things (IoT) has penetrated different aspects of our modern life where smart sensors enabled with Bluetooth Low Energy (BLE) are deployed increasingly within our surrounding indoor environments. BLE-based localization is, typically, performed based on Received Signal Strength Indicator (RSSI), which suffers from different drawbacks due to its significant fluctuations. In this paper, we focus on a multiplemodel estimation framework for analyzing and addressing effects of orientation of a BLE-enabled device on indoor localization accuracy. The fusion unit of the proposed method would merge orientation estimated by RSSI values and heading estimated by Inertial Measurement Unit (IMU) sensors to gain higher accuracy in orientation classification. In contrary to existing RSSIbased solutions that use a single path-loss model, the proposed framework consists of eight orientation-matched path loss models coupled with a multi-sensor and data-driven classification model that estimates the orientation of a hand-held device with high accuracy of 99%. By estimating the orientation, we could mitigate the effect of orientation on the RSSI values and consequently improve RSSI-based distance estimates. In particular, the proposed data-driven and multiple-model framework is constructed based on over 10 million RSSI values and IMU sensor data collected via an implemented LBS platform.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"83 1","pages":"1702-1706"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90325432","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: 11
The Smoothed Reassigned Spectrogram for Robust Energy Estimation 用于鲁棒能量估计的平滑重分配谱图
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287396
Erik Månsson, Maria Sandsten
{"title":"The Smoothed Reassigned Spectrogram for Robust Energy Estimation","authors":"Erik Månsson, Maria Sandsten","doi":"10.23919/Eusipco47968.2020.9287396","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287396","url":null,"abstract":"The matched window reassigned spectrogram relocates all signal energy of an oscillating transient to the time-and frequency locations, resulting in a sharp peak in the time-frequency plane. However, previous research has shown that the method may result in split energy peaks for close components and in high noise levels, and the peak energy is then erroneously estimated. With use of novel knowledge on the statistics when subjected to noise, we propose a novel method, the smoothed reassigned spectrogram, for obtaining a stable and accurate measure of the signal energy from the peak value, with retained resolution properties. We also suggest a simple set of rules to enhance the reassigned spectrogram and speed up its calculation. Simulations are performed to verify the accuracy and an application example on radar data is shown.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"17 1","pages":"2210-2214"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76700863","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
Transfer learning from speech to music: towards language-sensitive emotion recognition models 从语音到音乐的迁移学习:对语言敏感的情感识别模型
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287548
Juan Sebastián Gómez Cañón, Estefanía Cano, P. Herrera, E. Gómez
{"title":"Transfer learning from speech to music: towards language-sensitive emotion recognition models","authors":"Juan Sebastián Gómez Cañón, Estefanía Cano, P. Herrera, E. Gómez","doi":"10.23919/Eusipco47968.2020.9287548","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287548","url":null,"abstract":"In this study, we address emotion recognition using unsupervised feature learning from speech data, and test its transferability to music. Our approach is to pre-train models using speech in English and Mandarin, and then fine-tune them with excerpts of music labeled with categories of emotion. Our initial hypothesis is that features automatically learned from speech should be transferable to music. Namely, we expect the intra-linguistic setting (e.g., pre-training on speech in English and fine-tuning on music in English) should result in improved performance over the cross-linguistic setting (e.g., pre-training on speech in English and fine-tuning on music in Mandarin). Our results confirm previous research on cross-domain transferability, and encourage research towards language-sensitive Music Emotion Recognition (MER) models.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"50 1","pages":"136-140"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76791631","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
The Modulo Radon Transform and its Inversion 模Radon变换及其反演
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287586
A. Bhandari, Matthias Beckmann, F. Krahmer
{"title":"The Modulo Radon Transform and its Inversion","authors":"A. Bhandari, Matthias Beckmann, F. Krahmer","doi":"10.23919/Eusipco47968.2020.9287586","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287586","url":null,"abstract":"In this paper, we introduce the Modulo Radon Transform (MRT) which is complemented by an inversion algorithm. The MRT generalizes the conventional Radon Transform and is obtained via computing modulo of the line integral of a two-dimensional function at a given angle. Since the modulo operation has an aliasing effect on the range of a function, the recorded MRT sinograms are always bounded, thus avoiding information loss arising from saturation or clipping effects. This paves a new pathway for imaging applications such as high dynamic range tomography, a topic that is in its early stages of development. By capitalizing on the recent results on Unlimited Sensing architecture, we prove that the Modulo Radon Transform can be inverted when the resultant (discrete/continuous) measurements map to a band-limited function. Thus, the MRT leads to new possibilities for both conceptualization of inversion algorithms as well as development of new hardware, for instance, for single-shot high dynamic range tomography.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"201 1","pages":"770-774"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76983798","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: 21
Micro-Doppler Signal Representation for Drone Classification by Deep Learning 基于深度学习的无人机分类微多普勒信号表示
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287525
Julien Gérard, J. Tomasik, C. Morisseau, Arpad Rimmel, G. Vieillard
{"title":"Micro-Doppler Signal Representation for Drone Classification by Deep Learning","authors":"Julien Gérard, J. Tomasik, C. Morisseau, Arpad Rimmel, G. Vieillard","doi":"10.23919/Eusipco47968.2020.9287525","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287525","url":null,"abstract":"There are numerous formats which represent the micro-Doppler signature. Our goal is to determine which one is the most adapted to classify small UAV (Unmanned Aerial Vehicules) with Deep Learning. To achieve this goal, we compare drone classification results with the different micro-Doppler signatures for a given neural network. This comparison has been performed on data obtained during a radar measurement campaign. We evaluate the classification performance in function of different use conditions we identified with a given neural network. According to the experiments conducted, the recommended format is a spectrum issued from long observations as its classification results are better for most criteria.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"16 1","pages":"1561-1565"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75400838","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
Design of a Non-negative Neural Network to Improve on NMF 改进NMF的非负神经网络设计
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287668
Filip Wen-Fwu Tsai, Alireza M. Javid, S. Chatterjee
{"title":"Design of a Non-negative Neural Network to Improve on NMF","authors":"Filip Wen-Fwu Tsai, Alireza M. Javid, S. Chatterjee","doi":"10.23919/Eusipco47968.2020.9287668","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287668","url":null,"abstract":"For prediction of a non-negative target signal using a non-negative input, we design a feed-forward neural network to achieve a better performance than a non-negative matrix factorization (NMF) algorithm. We provide a mathematical relation between the neural network and NMF. The architecture of the neural network is built on a property of rectified-linear-unit (ReLU) activation function and a convex optimization layer-wise training approach. For an illustrative example, we choose a speech enhancement application where a clean speech spectrum is estimated from a noisy spectrum.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"38 1","pages":"461-465"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77909461","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
Unbiased FIR Filtering under Bernoulli-Distributed Binary Randomly Delayed and Missing Data 伯努利分布二进制随机延迟和缺失数据下的无偏FIR滤波
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287509
Karen J. Uribe-Murcia, J. Andrade-Lucio, Y. Shmaliy, Yuan Xu
{"title":"Unbiased FIR Filtering under Bernoulli-Distributed Binary Randomly Delayed and Missing Data","authors":"Karen J. Uribe-Murcia, J. Andrade-Lucio, Y. Shmaliy, Yuan Xu","doi":"10.23919/Eusipco47968.2020.9287509","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287509","url":null,"abstract":"This paper develops an unbiased finite impulse response (UFIR) filtering algorithm for networked systems where uncertain delays and packet dropouts can happen due to measurement failures and unreliable communication. The binary Bernoulli distribution with known delay probability is used to model the randomly arrived measures. A novel representation of the stochastic model is presented for FIR-type filter structures. To avoid packet dropouts and improve the estimation accuracy when a message arrives with no data, a predictive algorithm is used. An advantage of the UFIR filtering approach is demonstrated by comparing the mean square errors with the Kalman and H∞ filters under the same conditions. Experimental verifications are provided based on GPS vehicle tracking.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"38 1","pages":"2408-2412"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78254418","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
Full-Duplex mmWave Communication with Hybrid Precoding and Combining 基于混合预编码和组合的全双工毫米波通信
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287814
Roberto López-Valcarce, Marcos Martínez-Cotelo
{"title":"Full-Duplex mmWave Communication with Hybrid Precoding and Combining","authors":"Roberto López-Valcarce, Marcos Martínez-Cotelo","doi":"10.23919/Eusipco47968.2020.9287814","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287814","url":null,"abstract":"We investigate the design of hybrid precoders and combiners for a millimeter wave (mmWave) point-to-point bidirectional link in which both nodes transmit and receive simultaneously and on the same carrier frequency. In such full-duplex configuration, mitigation of self-interference (SI) becomes critical. Large antenna arrays provide an opportunity for spatial SI suppression in mmWave. We assume a phase-shifter based, fully connected architecture for the analog part of the precoder and combiner. The proposed design, which aims at cancelling SI in the analog domain to avoid frontend saturation, significantly improves on the performance of previous approaches.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"1752-1756"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74901476","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: 7
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization 利用基于注意力的序列到序列架构进行声音事件定位
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287224
C. Schymura, Tsubasa Ochiai, Marc Delcroix, K. Kinoshita, T. Nakatani, S. Araki, D. Kolossa
{"title":"Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization","authors":"C. Schymura, Tsubasa Ochiai, Marc Delcroix, K. Kinoshita, T. Nakatani, S. Araki, D. Kolossa","doi":"10.23919/Eusipco47968.2020.9287224","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287224","url":null,"abstract":"Sound event localization frameworks based on deep neural networks have shown increased robustness with respect to reverberation and noise in comparison to classical parametric approaches. In particular, recurrent architectures that incorporate temporal context into the estimation process seem to be well-suited for this task. This paper proposes a novel approach to sound event localization by utilizing an attention-based sequence-to-sequence model. These types of models have been successfully applied to problems in natural language processing and automatic speech recognition. In this work, a multi-channel audio signal is encoded to a latent representation, which is subsequently decoded to a sequence of estimated directions-of-arrival. Herein, attentions allow for capturing temporal dependencies in the audio signal by focusing on specific frames that are relevant for estimating the activity and direction-of-arrival of sound events at the current time-step. The framework is evaluated on three publicly available datasets for sound event localization. It yields superior localization performance compared to state-of-the-art methods in both anechoic and reverberant conditions.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"231-235"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72990309","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: 8
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信