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A novel feature selection in the case of brain PET image classification 脑PET图像分类中的一种新的特征选择方法
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362463
Imene Garali, M. Adel, S. Bourennane, E. Guedj
{"title":"A novel feature selection in the case of brain PET image classification","authors":"Imene Garali, M. Adel, S. Bourennane, E. Guedj","doi":"10.1109/EUSIPCO.2015.7362463","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362463","url":null,"abstract":"Positron Emission Tomography (PET) imaging is of importance for diagnosing neurodegenerative diseases like Alzheimer Disease (AD). Computer aided diagnosis methods could process and analyze quantitatively these images, in order to better characterize and extract meaningful information for medical diagnosis. This paper presents a novel computer-aided diagnosis technique for brain PET images classification in the case of AD. Brain images are first segmented into Regions Of Interest (ROI) using an atlas. Computing some statistical parameters on these regions, we define a Separation Power Factor (SPF) associated to each region. This factor quantifies the ability of each region to separate AD from Healthy Control (HC) brain images. Ranking selected regions according to their SPF and inputting them to a Support Vector Machine (SVM) classifier, yields better classification accuracy rate than when inputting the same number of ranked regions extracted from four others classical feature selection methods.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122851356","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
Deep neural networks for audio scene recognition 音频场景识别的深度神经网络
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362358
Y. Petetin, Cyrille Laroche, Aurélien Mayoue
{"title":"Deep neural networks for audio scene recognition","authors":"Y. Petetin, Cyrille Laroche, Aurélien Mayoue","doi":"10.1109/EUSIPCO.2015.7362358","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362358","url":null,"abstract":"These last years, artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), i.e. ANN with at least two hidden layers. In the same time, the computational auditory scene recognition (CASR) problem which consists in estimating the environment around a device from the received audio signal has been investigated. Most of works which deal with the CASR problem have tried to ind well-adapted features for this problem. However, these features are generally combined with a classical classi-ier. In this paper, we introduce DNN in the CASR ield and we show that such networks can provide promising results and perform better than standard classiiers when the same features are used.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114143475","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: 40
Array calibration using array response interpolation and parametric modeling 使用阵列响应插值和参数化建模的阵列校准
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362601
Bin Yang, T. McKelvey, M. Viberg, Guanghan Xu
{"title":"Array calibration using array response interpolation and parametric modeling","authors":"Bin Yang, T. McKelvey, M. Viberg, Guanghan Xu","doi":"10.1109/EUSIPCO.2015.7362601","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362601","url":null,"abstract":"High-performance array applications often require an accurate array response model. A common way to achieve this is by array calibration which involves measuring the response for a finite number of given source directions and employing interpolation. This paper considers the array calibration problem by combing interpolation techniques and parametric modeling. The idea is to model the array response as a product of a mutual coupling matrix, an ideal array response vector (derived from the geometry of antenna array) and an angle-dependent correction vector. Since the major effects are captured by the physical model and the mutual coupling matrix, the correction vector will be a smoother function of angle as compared to direct interpolation of the measured array response. In numerical experiments of a real antenna array, the method is found to improve the performance of the array calibration significantly.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117037472","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
Multi-modal service operation estimation using DNN-based acoustic bag-of-features 基于dnn的声学特征袋的多模态业务估计
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362793
S. Tamura, Takuya Uno, Masanori Takehara, S. Hayamizu, T. Kurata
{"title":"Multi-modal service operation estimation using DNN-based acoustic bag-of-features","authors":"S. Tamura, Takuya Uno, Masanori Takehara, S. Hayamizu, T. Kurata","doi":"10.1109/EUSIPCO.2015.7362793","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362793","url":null,"abstract":"In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities; particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and bag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multimodal SOE scheme is also clarified.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128214107","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
Maximum likelihood and robust G-music performance in K-distributed noise k分布噪声中的极大似然和稳健G-music性能
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362687
Y. Abramovich, Ben A. Johnson, O. Besson
{"title":"Maximum likelihood and robust G-music performance in K-distributed noise","authors":"Y. Abramovich, Ben A. Johnson, O. Besson","doi":"10.1109/EUSIPCO.2015.7362687","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362687","url":null,"abstract":"For an antenna array input mixture of m point source signals in K-distributed noise, we compare DOA estimation delivered by Maximum Likelihood and the recently introduced Robust G-MUSIC (RG-MUSIC) technique. We demonstrate that similar to the Gaussian case, MLE is still superior to RG-MUSIC, especially within the so-called threshold region. This makes it possible to use the Expected Likelihood (EL) methodology to detect the presence of RG-MUSIC performance breakdown and \"cure\" those cases via an approach previously developed for the complex Gaussian circumstance.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128546482","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
Energy efficient monitoring of activities of daily living using wireless acoustic sensor networks in clean and noisy conditions 在清洁和嘈杂的条件下，使用无线声学传感器网络对日常生活活动进行节能监测
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362423
L. Vuegen, B. V. Broeck, P. Karsmakers, H. V. hamme, B. Vanrumste
{"title":"Energy efficient monitoring of activities of daily living using wireless acoustic sensor networks in clean and noisy conditions","authors":"L. Vuegen, B. V. Broeck, P. Karsmakers, H. V. hamme, B. Vanrumste","doi":"10.1109/EUSIPCO.2015.7362423","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362423","url":null,"abstract":"This work examines the use of a Wireless Acoustic Sensor Network (WASN) for the classification of clinically relevant activities of daily living (ADL) from elderly people. The aim of this research is to automatically compile a summary report about the performed ADLs which can be easily interpreted by caregivers. In this work the classification performance of the WASN will be evaluated in both clean and noisy conditions. Moreover, the computational complexity of the WASN and solutions to reduce the required computational costs are examined as well. The obtained classification results indicate that the computational cost can be reduced by a factor of 2.43 without a significant loss in accuracy. In addition, the WASN yields a 1.4% to 4.8% increase in classification accuracy in noisy conditions compared to single microphone solutions.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128644507","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
Daily activity recognition based on DNN using environmental sound and acceleration signals 基于深度神经网络的日常活动识别，使用环境声音和加速度信号
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362796
Tomoki Hayashi, M. Nishida, N. Kitaoka, K. Takeda
{"title":"Daily activity recognition based on DNN using environmental sound and acceleration signals","authors":"Tomoki Hayashi, M. Nishida, N. Kitaoka, K. Takeda","doi":"10.1109/EUSIPCO.2015.7362796","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362796","url":null,"abstract":"We propose a new method of recognizing daily human activities based on a Deep Neural Network (DNN), using multimodal signals such as environmental sound and subject acceleration. We conduct recognition experiments to compare the proposed method to other methods such as a Support Vector Machine (SVM), using real-world data recorded continuously over 72 hours. Our proposed method achieved a frame accuracy rate of 85.5% and a sample accuracy rate of 91.7% when identifying nine different types of daily activities. Furthermore, the proposed method outperformed the SVM-based method when an additional \"Other\" activity category was included. Therefore, we demonstrate that DNNs are a robust method of daily activity recognition.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128988801","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: 38
A kernel based technique for MSER equalisation for non-linear channels 非线性信道中基于核的MSER均衡技术
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362623
R. Mitra, V. Bhatia
{"title":"A kernel based technique for MSER equalisation for non-linear channels","authors":"R. Mitra, V. Bhatia","doi":"10.1109/EUSIPCO.2015.7362623","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362623","url":null,"abstract":"Adaptive channel equalisation is a signal processing technique to mitigate inter-symbol interference (ISI) in a time dispersive channel. To this end, the use of least mean squares (LMS) algorithm and its variants is widespread since they minimise the minimum mean squared error (MMSE) criteria by online stochastic gradient algorithms and they asymptotically tend to the optimal Weiner solution for linearly separable channels. The kernel least mean squares (KLMS) algorithm and its variants are based on the MMSE based algorithms for non-linear channels. However, as has been pointed out in the literature, the minimum bit/symbol error rate (MBER/MSER) criterion is a better choice for adapting an equaliser as compared to the traditional approaches based on MMSE criterion. In this paper, we propose a novel equaliser that is inspired from the recently proposed MSER adaptation by Gong et al. using the kernel trick for non-linear channel equalisation.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129259720","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
Discovery of repeated vocal patterns in polyphonic audio: A case study on flamenco music 复调音频中重复声乐模式的发现:以弗拉门戈音乐为例
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362341
N. Kroher, A. Pikrakis, J. Moreno, J. Díaz-Báñez
{"title":"Discovery of repeated vocal patterns in polyphonic audio: A case study on flamenco music","authors":"N. Kroher, A. Pikrakis, J. Moreno, J. Díaz-Báñez","doi":"10.1109/EUSIPCO.2015.7362341","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362341","url":null,"abstract":"This paper presents a method for the discovery of repeated vocal patterns directly from music recordings. At a first stage, a voice detection algorithm provides a rough segmentation of the recording to vocal parts, based on which an estimate of the average pattern duration is computed. Then, a pattern detector which employs a sequence alignment algorithm is used to yield a ranking of pairs of matches of the detected voiced segments. At a last stage, a clustering algorithm produces the final repeated patterns. Our method was evaluated in the context of flamenco music for which symbolic metadata are very hard to produce, yielding very promising results.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129429699","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
Local relative transfer function for sound source localization 声源定位的局部相对传递函数
2015 23rd European Signal Processing Conference (EUSIPCO) Pub Date : 2015-12-28 DOI: 10.1109/EUSIPCO.2015.7362413
Xiaofei Li, R. Horaud, Laurent Girin, S. Gannot
{"title":"Local relative transfer function for sound source localization","authors":"Xiaofei Li, R. Horaud, Laurent Girin, S. Gannot","doi":"10.1109/EUSIPCO.2015.7362413","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2015.7362413","url":null,"abstract":"The relative transfer function (RTF), i.e. the ratio of acoustic transfer functions between two sensors, can be used for sound' source localization / beamforming based on a microphone array. The RTF is usually defined with respect to a unique reference sensor. Choosing the reference sensor may be a difficult task, especially for dynamic acoustic environment and setup. In this paper we propose to use a locally normalized RTF, in short local-RTF, as an acoustic feature to characterize the source direction. Local-RTF takes a neighbor sensor as the reference channel for a given sensor. The estimated local-RTF vector can thus avoid the bad effects of a noisy unique reference and have smaller estimation error than conventional RTF estimators. We propose two estimators for the local-RTF and concatenate the values across sensors and frequencies to form a high-dimensional vector which is utilized for source localization. Experiments with real-world signals show the interest of this approach.","PeriodicalId":401040,"journal":{"name":"2015 23rd European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793028","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
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