Intelligent Automation of Acoustic Classification Using Independent Component Analysis and Competitive Neural Nets

使用独立分量分析和竞争神经网络的声学分类智能自动化

• 批准号: 17360185
• 负责人: OMATU Sigeru
• 金额: $ 8.42万
• 依托单位: Osaka Prefecture University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17360185/
• 关键词: independent component analysis; Signal detection; Acoustic test; classification of products; spectral analysis; genetic algorithm; intellieent sienal nrocessin; Acoustic inspection device; 匂い計測装置; オクターブバンド; 自己組織化特徴地図; 競合型ニューラルネットワーク; 匂い特徴量; 匂

In this research, we have set the final goal to achieve the intelligent acoustic classification system with compact size by using independent component analysis (ICA) and competitive neural networks. Using the ICA, we have developed the ICA noise separation system to obtain excellent separation of signals and noises and applied it to real acoustic data of compact shaving machine, which were observed under very noisy condition.Then the separated acoustic signals were transformed into frequency domain by using Fourier transform. From the spectrum we have decided the feature vector to classify the compact shavers into good or not based on the competitive neural networks. The adopted neural network is the Kohonen's network of self-organizing feature map (SOM). Using the SOM, we select the most effective feature vector of the spectrum of acoustic data. Then we apply another competitive neural network of learning vector quantization (LVQ). Finally, we have tested the other products such as massage machine, caps of illumination lamps, bearing shaft, etc.

在本研究中，我们的最终目标是通过使用独立分量分析（伊卡）和竞争神经网络来实现尺寸紧凑的智能声学分类系统。利用独立分量分析（伊卡，建立了伊卡噪声分离系统，实现了信号与噪声的良好分离，并将其应用于小型剃齿机在强噪声条件下的真实的声学数据，然后对分离后的声学信号进行傅立叶变换，得到频率域信号。从频谱中，我们已经决定了特征向量，以分类的紧凑型剃须刀到好或不基于竞争的神经网络。所采用的神经网络是Kohonen的自组织特征映射（SOM）网络。利用自组织神经网络，我们选择了最有效的特征向量的频谱声波数据。然后，我们采用另一种竞争神经网络的学习矢量量化（LVQ）。最后，我们对其他产品如按摩机、照明灯灯头、轴承轴等进行了测试。

项目成果

Remote Sensing Image Analysis Using Neural Networks

使用神经网络进行遥感图像分析

• 发表时间: 2007
• 作者: Tomohisa;Konishi
• 通讯作者: Konishi

Taylor and Francis Books

泰勒和弗朗西斯书籍

• 发表时间: 2006
• 作者: Tomohisa;Konishi;Michifumi Yoshioka
• 通讯作者: Michifumi Yoshioka

Text Categorization Using Earth Mover's Distance as Similarity Measure

使用推土机距离作为相似性度量的文本分类

• 发表时间: 2007
• 作者: Hidekazu;Yanagimoto
• 通讯作者: Yanagimoto

「研究成果報告書概要(和文)」より

摘自《研究结果报告摘要（日文）》

• 发表时间: 2005
• 作者: Kawauchi;et. al.;Nishimura et al.;Dezawa et al.;Yoshizawa et al.;星野 幹雄;星野 幹雄
• 通讯作者: 星野 幹雄

Decomposition and Reconstruction of Signal in Real-Time Spectral Analysis

实时频谱分析中信号的分解和重构

• 发表时间: 2007
• 期刊: IEEJ Trans. on EIS Vol.127 No.7
• 作者: Hideto;Nakatsuji
• 通讯作者: Nakatsuji