Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing最新文献

Time domain blind source separation of non-stationary convolved signals by utilizing geometric beamforming 基于几何波束形成的非平稳卷积信号时域盲源分离

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030056

R. Aichner, S. Araki, S. Makino, T. Nishikawa, H. Saruwatari

{"title":"Time domain blind source separation of non-stationary convolved signals by utilizing geometric beamforming","authors":"R. Aichner, S. Araki, S. Makino, T. Nishikawa, H. Saruwatari","doi":"10.1109/NNSP.2002.1030056","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030056","url":null,"abstract":"We propose a time-domain blind source separation (BSS) algorithm that utilizes geometric information such as sensor positions and assumed locations of sources. The algorithm tackles the problem of convolved mixtures by explicitly exploiting the non-stationarity of the acoustic sources. The learning rule is based on second-order statistics and is derived by natural gradient minimization. The proposed initialization of the algorithm is based on the null beamforming principle. This method leads to improved separation performance, and the algorithm is able to estimate long unmixing FIR filters in the time domain due to the geometric initialization. We also propose a post-filtering method for dewhitening which is based on the scaling technique in frequency-domain BSS. The validity of the proposed method is shown by computer simulations. Our experimental results confirm that the algorithm is capable of separating real-world speech mixtures and can be applied to short learning data sets down to a few seconds. Our results also confirm that the proposed dewhitening post-filtering method maintains the spectral content of the original speech in the separated output.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125416979","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: 55

Statistical descriptor of normality based on Hotelling's T/sup 2/ statistic and mixture of Gaussians 基于Hotelling的T/sup 2/统计量和混合高斯量的正态性统计描述符

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030052

A. Dolia

引用次数: 1

Facial expression analysis using shape and motion information extracted by convolutional neural networks 基于卷积神经网络提取形状和运动信息的面部表情分析

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030072

B. Fasel

引用次数: 12

Finding temporal structure in music: blues improvisation with LSTM recurrent networks 寻找音乐中的时间结构:用LSTM循环网络进行蓝调即兴创作

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030094

D. Eck, J. Schmidhuber

{"title":"Finding temporal structure in music: blues improvisation with LSTM recurrent networks","authors":"D. Eck, J. Schmidhuber","doi":"10.1109/NNSP.2002.1030094","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030094","url":null,"abstract":"We consider the problem of extracting essential ingredients of music signals, such as a well-defined global temporal structure in the form of nested periodicities (or meter). We investigate whether we can construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style. Because recurrent neural networks (RNNs) can, in principle, learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard RNNs often lacks global coherence. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long short-term memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing and counting and the learning of context sensitive languages. We show that LSTM is also a good mechanism for learning to compose music. We present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and we believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure, it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132329660","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: 245

Parallel and separable recursive Levenberg-Marquardt training algorithm 并行和可分离递归Levenberg-Marquardt训练算法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030024

V. Asirvadam, S. McLoone, G. Irwin

引用次数: 36

An efficient SMO-like algorithm for multiclass SVM 多类支持向量机的一种高效类smo算法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030041

F. Aiolli, A. Sperduti

引用次数: 14

A two-stage SVM architecture for predicting the disulfide bonding state of cysteines 半胱氨酸二硫键态预测的两阶段支持向量机结构

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030014

P. Frasconi, Andrea Passerini, A. Vullo

引用次数: 40

Feature selection for off-line recognition of different size signatures 不同大小签名离线识别的特征选择

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030047

George D. C. Cavalcanti, Rodrigo C. Doria, E. C. B. C. Filho

引用次数: 13

Simple algorithms for decorrelation-based blind source separation 基于去相关的盲源分离的简单算法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030066

S. Douglas

引用次数: 3

Analysis of support vector machines 支持向量机分析

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030020

S. Abe

引用次数: 26