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

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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287734
Saori Takeyama, Tatsuya Komatsu, Koichi Miyazaki, M. Togami, Shunsuke Ono
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.
基于最大分类器差异(MCD)和知识蒸馏(KD),提出了多设备的鲁棒声场景分类方法。该方法采用域自适应的方法来训练多个专用于每个设备的ASC模型,并使用KD技术将这些多个特定于设备的模型组合成一个多域ASC模型。在领域自适应方面,本文提出的方法利用MCD来对齐类分布,这是传统的用于ASC的DA方法所忽略的。多设备鲁棒ASC模型是通过KD获得的,结合MCD的多设备特定ASC模型,这些模型对于非目标设备可能具有较低的性能。我们的实验表明,所提出的基于mcd的设备特定模型对目标样本的ASC精度提高了最多12.22%,而所提出的基于kd的设备通用模型对所有设备的ASC精度平均提高了2.13%。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287508
Taishi Nakashima, Robin Scheibler, Yukoh Wakabayashi, Nobutaka Ono
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.
本文提出了一种三源或多源独立低秩矩阵分析(ILRMA)算法,该算法比传统的ILRMA算法更快。在传统的ILRMA中,分解向量是通过迭代投影(IP)法逐个更新的。通过对ILRMA的目标函数对解混向量求导得到一个二次方程组,推导出IP的更新规则。这种二次方程系统被称为混合精确近似联合对角化(HEAD),对于三个或更多的源,目前还没有已知的闭型解。近年来,提出了一种同时更新两个分离矢量的独立矢量分析方法。该方法通过将两个源的HEAD简化为一个广义特征值问题并求解得到。此外,成对更新最近已扩展到三个或更多来源的情况。然而,对ILRMA的成对更新的有效性尚未进行研究。因此,在这项工作中,我们将分解向量的成对更新应用于ILRMA。通过将分解向量的更新规则替换为所提出的两两更新规则,加快了ILRMA的收敛速度。实验结果表明,该方法比传统的ILRMA具有更快的收敛速度和更好的性能。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287698
Michael Perlmutter, N. Sissouno, A. Viswanathan, M. Iwen
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.
我们提出了一种与直接相位检索方法密切相关的算法,该方法已被证明在经验上工作得很好[1],[2],并证明它保证从它们的谱图测量中恢复(直到全局相位)一大类紧支持平滑函数。因此,我们朝着开发一种新型实用的无相成像算法迈出了第一步,这种算法能够在被固定周期光栅的几个移位掩盖后产生可证明的精确图像。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287739
Niccoló Nicodemo, Gaurav Naithani, K. Drossos, T. Virtanen, R. Saletti
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%.
深度神经网络(dnn)在移动设备和嵌入式系统(如现场可编程门阵列)中的有效应用受到内存和计算能力要求的阻碍。本文提出了一种采用非均匀不动点量化和虚拟位移位(VBS)的方法来提高深度神经网络权重量化的精度。我们在语音增强应用中评估了我们的方法,其中使用全连接DNN从输入噪声语音频谱中预测干净的语音频谱。对深度神经网络进行优化,计算其内存需求,并使用短时客观可理解度(STOI)指标评估其性能。低比特量化的应用导致DNN内存需求降低50%,而STOI性能仅下降2.7%。
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引用次数: 3
PRNU-leaks: facts and remedies prnu泄漏:事实和补救措施
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287451
F. Pérez-González, Samuel Fernández-Menduiña
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.
我们解决了传感器的光响应非均匀性(PRNU)指纹估计的信息泄漏问题。这种泄漏可能会损害取证场景中的隐私,因为它可能会泄露PRNU估计中使用的图像中的信息。我们提出了一种基于嵌入合成PRNUs的计算信息论泄漏的新方法,并给出了可承受的近似值和边界。我们还提出了一种新的精简度量方法来衡量隶属推理测试的性能。最后,我们分析了两种潜在的防泄漏对策:二值化和均衡。二值化是一种已经在prnu存储环境中使用的方法,而均衡是一种新颖且性能更好的方法。在实际图像数据集上进行了实验,验证了理论结果。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287452
P. Bonizzi, S. Zeemering, Frank van Rosmalen, U. Schotten, Joël M. H. Karel
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.
心房颤动(AF)时心房活动的传播是一种复杂的现象,具有一定程度的复发性(周期性重复)。在这项研究中,我们研究了从体表电位图记录中无创检测持续性房颤患者复发的可能性,并在时间和空间上定位这种复发。结果表明,这些患者的体表记录可识别复发集群。此外,电复律4至6周后,检测到的簇数及其在躯干背部右上方的位置与房颤复发显著相关。这表明对持续性房颤患者的复发进行无创量化是可能的,并可能有助于改善患者分层。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287345
Haoyuan Cai, M. Kaloorazi, Jie Chen, Wei Chen, C. Richard
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.
广义厄米特征分解问题在信号和机器学习应用中普遍存在。考虑到实际中处理流数据的需要和现有方法的局限性,本文研究了快速有效的广义特征向量跟踪方法。我们首先提出了一种基于随机化的计算效率高的算法,称为交替投影随机特征值分解(APR-EVD)来解决标准特征值问题。利用rank-1策略,提出了两种基于APR-EVD的优势广义特征向量在线提取算法。数值算例表明了所提在线算法的实用性和有效性。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287589
Cem Ates Musluoglu, A. Bertrand
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.
迹比优化问题包括最大化两个迹算子之间的比值,经常出现在去噪或判别分析的降维问题中。在本文中,我们提出了一种分布式和自适应算法来解决网络范围内协方差矩阵的跟踪比率优化问题,该问题捕获了无线传感器网络中传感器之间的空间相关性。我们专注于全连接网络拓扑,在这种情况下,分布式算法通过在每个给定节点上仅共享观测信号的压缩版本来减少通信瓶颈。尽管有这种压缩,但可以证明该算法收敛到最大跟踪比,就好像所有节点都可以访问网络中的所有信号一样。仿真结果证明了该算法的收敛性和最优性。
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引用次数: 0
Fractional Superlets 部分Superlets
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287873
Harald Bârzan, V. V. Moca, Ana-Maria Ichim, R. Muresan
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.
连续小波变换(CWT)通过缩放母小波并将其与信号进行卷积来提供信号的多分辨率表示。然后,尺度图(CWT的平方模量)表示信号能量的扩散作为时间和尺度的函数。尺度图具有恒定的相对时间分辨率,但随着尺度被压缩(频率增加),它会失去频率分辨率。为了弥补这一点,最近引入的超小波以几何方式组合了一组频率分辨率越来越高的小波,以实现时频超分辨率。集合中小波的数量称为超小波的阶数,最初定义为整数。这在实现自适应超let时产生了一系列问题,例如,超let的顺序取决于频率。特别是,自适应超小波产生的表示会受到“带状”的影响,因为随着频率的增加,顺序会以离散的步骤进行调整。在这里,通过依赖加权几何平均值,我们引入了分数阶超小波,它允许阶是分数阶。我们表明,分数自适应超小波提供了在整个光谱上平滑的高分辨率表示,并且明显优于基于离散自适应超小波的表示。
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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
Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287443
L. Villa, Mirco Pezzoli, F. Antonacci, A. Sarti
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.
本文提出了一种估计音色木材纵波速度的方法。与制琴领域采用的技术不同,所提出的估算方法既不需要特定的用户技能,也不需要昂贵的仪器。所介绍的方法利用木块的脉冲响应,由加速度计获得。测量的信号被处理,以便计算一个估计的音色木材的纵波速度在一个耙接收器的方式。我们在合成数据和实际色调木块的测量上测试了该技术,显示了所提出的解决方案相对于最先进的方法的有效性。
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引用次数: 0
期刊
2020 28th European Signal Processing Conference (EUSIPCO)
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