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Robust Acoustic Scene Classification to Multiple Devices Using Maximum Classifier Discrepancy and Knowledge Distillation 基于最大分类器差异和知识蒸馏的多设备声场景鲁棒分类
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287734
Saori Takeyama, Tatsuya Komatsu, Koichi Miyazaki, M. Togami, Shunsuke Ono
{"title":"Robust Acoustic Scene Classification to Multiple Devices Using Maximum Classifier Discrepancy and Knowledge Distillation","authors":"Saori Takeyama, Tatsuya Komatsu, Koichi Miyazaki, M. Togami, Shunsuke Ono","doi":"10.23919/Eusipco47968.2020.9287734","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287734","url":null,"abstract":"This paper proposes robust acoustic scene classification (ASC) to multiple devices using maximum classifier discrepancy (MCD) and knowledge distillation (KD). The proposed method employs domain adaptation to train multiple ASC models dedicated to each device and combines these multiple device-specific models using a KD technique into a multi-domain ASC model. For domain adaptation, the proposed method utilizes MCD to align class distributions that conventional DA for ASC methods have ignored. The multi-device robust ASC model is obtained by KD, combining the multiple device-specific ASC models by MCD that may have a lower performance for non-target devices. Our experiments show that the proposed MCD-based device-specific model improved ASC accuracy by at most 12.22% for target samples, and the proposed KD-based device-general model improved ASC accuracy by 2.13% on average for all devices.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"14 1","pages":"36-40"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88907855","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: 9
Faster independent low-rank matrix analysis with pairwise updates of demixing vectors 更快的独立低秩矩阵分析与分解向量的两两更新
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287508
Taishi Nakashima, Robin Scheibler, Yukoh Wakabayashi, Nobutaka Ono
{"title":"Faster independent low-rank matrix analysis with pairwise updates of demixing vectors","authors":"Taishi Nakashima, Robin Scheibler, Yukoh Wakabayashi, Nobutaka Ono","doi":"10.23919/Eusipco47968.2020.9287508","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287508","url":null,"abstract":"In this paper, we present an algorithm for independent low-rank matrix analysis (ILRMA) of three or more sources that is faster than that for conventional ILRMA. In conventional ILRMA, demixing vectors are updated one by one by the iterative projection (IP) method. The update rules of IP are derived from a system of quadratic equations obtained by differentiating the objective function of ILRMA with respect to demixing vectors. This system of quadratic equations is called hybrid exact-approximate joint diagonalization (HEAD) and no closed-form solution is known yet for three or more sources. Recently, a method that can update two demixing vectors simultaneously has been proposed for independent vector analysis. The method is derived by reducing HEAD for two sources to a generalized eigenvalue problem and solving the problem. Furthermore, the pairwise updates have recently been extended to the case of three or more sources. However, the efficacy of the pairwise updates for ILRMA has not yet been investigated. Therefore, in this work, we apply the pairwise updates of demixing vectors to ILRMA. By replacing the update rules of demixing vectors with the proposed pairwise updates, we accelerate the convergence of ILRMA. The experimental results show that the proposed method yields faster convergence and better performance than conventional ILRMA.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"301-305"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87612643","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
A Provably Accurate Algorithm for Recovering Compactly Supported Smooth Functions from Spectrogram Measurements 从谱图测量中恢复紧支持光滑函数的一种可证明的精确算法
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287698
Michael Perlmutter, N. Sissouno, A. Viswanathan, M. Iwen
{"title":"A Provably Accurate Algorithm for Recovering Compactly Supported Smooth Functions from Spectrogram Measurements","authors":"Michael Perlmutter, N. Sissouno, A. Viswanathan, M. Iwen","doi":"10.23919/Eusipco47968.2020.9287698","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287698","url":null,"abstract":"We present an algorithm which is closely related to direct phase retrieval methods that have been shown to work well empirically [1], [2] and prove that it is guaranteed to recover (up to a global phase) a large class of compactly supported smooth functions from their spectrogram measurements. As a result, we take a first step toward developing a new class of practical phaseless imaging algorithms capable of producing provably accurate images of a given sample after it is masked by just a few shifts of a fixed periodic grating.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"35 1","pages":"970-974"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90328637","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
Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters 基于低比特参数量化的现场可编程门阵列深度神经网络内存需求降低
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287739
Niccoló Nicodemo, Gaurav Naithani, K. Drossos, T. Virtanen, R. Saletti
{"title":"Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters","authors":"Niccoló Nicodemo, Gaurav Naithani, K. Drossos, T. Virtanen, R. Saletti","doi":"10.23919/Eusipco47968.2020.9287739","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287739","url":null,"abstract":"Effective employment of deep neural networks (DNNs) in mobile devices and embedded systems, like field programmable gate arrays, is hampered by requirements for memory and computational power. In this paper we propose a method that employs a non-uniform fixed-point quantization and a virtual bit shift (VBS) to improve the accuracy of the quantization of the DNN weights. We evaluate our method in a speech enhancement application, where a fully connected DNN is used to predict the clean speech spectrum from the input noisy speech spectrum. A DNN is optimized, its memory requirement is calculated, and its performance is evaluated using the short-time objective intelligibility (STOI) metric. The application of the low-bit quantization leads to a 50% reduction of the DNN memory requirement while the STOI performance drops only by 2.7%.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"50 1","pages":"466-470"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86000385","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
PRNU-leaks: facts and remedies prnu泄漏:事实和补救措施
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287451
F. Pérez-González, Samuel Fernández-Menduiña
{"title":"PRNU-leaks: facts and remedies","authors":"F. Pérez-González, Samuel Fernández-Menduiña","doi":"10.23919/Eusipco47968.2020.9287451","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287451","url":null,"abstract":"We address the problem of information leakage from estimates of the PhotoResponse Non-Uniformity (PRNU) fingerprints of a sensor. This leakage may compromise privacy in forensic scenarios, as it may reveal information from the images used in the PRNU estimation. We propose a new way to compute the information-theoretic leakage that is based on embedding synthetic PRNUs, and presesent affordable approximations and bounds. We also propose a new compact measure for the performance in membership inference tests. Finally, we analyze two potential countermeasures against leakage: binarization, which was already used in PRNU-storage contexts, and equalization, which is novel and offers better performance. Theoretical results are validated with experiments carried out on a real-world image dataset.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"34 1","pages":"720-724"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86034936","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
Noninvasive Assessment of Spatio-Temporal Recurrence in Atrial Fibrillation 心房颤动时空复发的无创评估
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287452
P. Bonizzi, S. Zeemering, Frank van Rosmalen, U. Schotten, Joël M. H. Karel
{"title":"Noninvasive Assessment of Spatio-Temporal Recurrence in Atrial Fibrillation","authors":"P. Bonizzi, S. Zeemering, Frank van Rosmalen, U. Schotten, Joël M. H. Karel","doi":"10.23919/Eusipco47968.2020.9287452","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287452","url":null,"abstract":"Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"900-904"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84716172","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
Online Dominant Generalized Eigenvectors Extraction Via A Randomized Method 基于随机化方法的在线优势广义特征向量提取
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287345
Haoyuan Cai, M. Kaloorazi, Jie Chen, Wei Chen, C. Richard
{"title":"Online Dominant Generalized Eigenvectors Extraction Via A Randomized Method","authors":"Haoyuan Cai, M. Kaloorazi, Jie Chen, Wei Chen, C. Richard","doi":"10.23919/Eusipco47968.2020.9287345","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287345","url":null,"abstract":"The generalized Hermitian eigendecomposition problem is ubiquitous in signal and machine learning applications. Considering the need of processing streaming data in practice and restrictions of existing methods, this paper is concerned with fast and efficient generalized eigenvectors tracking. We first present a computationally efficient algorithm based on randomization termed alternate-projections randomized eigenvalue decomposition (APR-EVD) to solve a standard eigenvalue problem. By exploiting rank-1 strategy, two online algorithms based on APR-EVD are developed for the dominant generalized eigenvectors extraction. Numerical examples show the practical applicability and efficacy of the proposed online algorithms.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"41 1","pages":"2353-2357"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85128616","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
Distributed Trace Ratio Optimization in Fully-Connected Sensor Networks 全连接传感器网络中的分布式走线比优化
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287589
Cem Ates Musluoglu, A. Bertrand
{"title":"Distributed Trace Ratio Optimization in Fully-Connected Sensor Networks","authors":"Cem Ates Musluoglu, A. Bertrand","doi":"10.23919/Eusipco47968.2020.9287589","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287589","url":null,"abstract":"The trace ratio optimization problem consists of maximizing a ratio between two trace operators and often appears in dimensionality reduction problems for denoising or discriminant analysis. In this paper, we propose a distributed and adaptive algorithm to solve the trace ratio optimization problem over network-wide covariance matrices, which capture the spatial correlation across sensors in a wireless sensor network. We focus on fully-connected network topologies, in which case the distributed algorithm reduces the communication bottleneck by only sharing a compressed version of the observed signals at each given node. Despite this compression, the algorithm can be shown to converge to the maximal trace ratio as if all nodes would have access to all signals in the network. We provide simulation results to demonstrate the convergence and optimality properties of the proposed algorithm.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"20 1","pages":"1991-1995"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84284537","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
Fractional Superlets 部分Superlets
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287873
Harald Bârzan, V. V. Moca, Ana-Maria Ichim, R. Muresan
{"title":"Fractional Superlets","authors":"Harald Bârzan, V. V. Moca, Ana-Maria Ichim, R. Muresan","doi":"10.23919/Eusipco47968.2020.9287873","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287873","url":null,"abstract":"The Continuous Wavelet Transform (CWT) provides a multi-resolution representation of a signal by scaling a mother wavelet and convolving it with the signal. The scalogram (squared modulus of the CWT) then represents the spread of the signal's energy as a function of time and scale. The scalogram has constant relative temporal resolution but, as the scale is compressed (frequency increased), it loses frequency resolution. To compensate for this, the recently-introduced superlets geometrically combine a set of wavelets with increasing frequency resolution to achieve time-frequency super-resolution. The number of wavelets in the set is called the order of the superlet and was initially defined as an integer number. This creates a series of issues when adaptive superlets are implemented, i.e. superlets whose order depends on frequency. In particular, adaptive superlets generate representations that suffer from \"banding\" because the order is adjusted in discrete steps as the frequency increases. Here, by relying on the weighted geometric mean, we introduce fractional superlets, which allow the order to be a fractional number. We show that fractional adaptive superlets provide high-resolution representations that are smooth across the entire spectrum and are clearly superior to representations based on the discrete adaptive superlets.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"2220-2224"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88303668","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
A Methodology for the Estimation of Propagation Speed of Longitudinal Waves in Tone Wood 一种估计音色木材中纵波传播速度的方法
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287443
L. Villa, Mirco Pezzoli, F. Antonacci, A. Sarti
{"title":"A Methodology for the Estimation of Propagation Speed of Longitudinal Waves in Tone Wood","authors":"L. Villa, Mirco Pezzoli, F. Antonacci, A. Sarti","doi":"10.23919/Eusipco47968.2020.9287443","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287443","url":null,"abstract":"In this paper we propose a methodology for the estimation of the longitudinal wave velocity in tone wood. Differently from techniques adopted in the field of luthiery, the proposed estimation method does not require neither specific user skill nor expensive instrumentation. The introduced method exploits the impulse response of the wood block, acquired by means of accelerometers. The measured signals are processed in order to compute an estimate of the longitudinal wave velocity of the tone wood in a rake receiver fashion. We tested the technique both on synthetic data and measurements of actual tone wood blocks, showing the effectiveness of the proposed solution with respect to state-of-the-art methods.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"124 1","pages":"66-70"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88060596","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
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