Akira Morimoto

A new design based on wavelet analysis for the objective audiometry devices is proposed. The auditory brainstem response and 80-Hz auditory steady-state response (ASSR) are used in the objective audiometry devices for infants. For the aged, an objective audiometry device is used in anti-aging investigations, which enables the hearing acuity of awake adults to be tested with the 40-Hz ASSR. The ASSR evoked by an amplitude modulated tone is recorded as a waveform. However, the evoked potential response is very small. Therefore, it is difficult to decide a threshold of the response and whether a significant response exists when it is mixed with noise such as the background brain waves. To cope with this problem, we need to average the evoked response waveforms. In particular, the 40-Hz ASSR has a large amount of noise caused by the background brain waves in comparison with the 80-Hz ASSR. In this paper, we apply waveform analysis using the wavelet transform in order to extract the 40-Hz ASSR from a signal mixed with a large amount of noise. Subjects with normal hearing participated in this study. © 2012 IEEE.

The blind image source separation problem is to estimate the number of the unknown source images and to separate the original images from superpositions of source images. The coefficients of the superposition are also unknown. Our proposed source reduction method has a potential to overcome the difficulty of previously proposed methods. In this paper, we give an algorithm of the method with continuous multiwavelet transform for the image separation. Various numerical experiments using the proposed algorithm ensure that our proposed source reduction method performs well for image separation. An example of numerical experiments is presented. © 2012 IEEE.

The blind source separation problem is to estimate the number of the unknown source signals and to separate the original source signals from the several observed signals, which are assumed to be linear superpositions of the sources. The coefficients of the superposition are also unknown. In this paper, in order to overcome a difficulty in previously proposed methods, a new approach to solve the problem, named source reduction method, using continuous wavelet transform is proposed. © 2012 IEEE.

The purpose of blind source separation is to separate and to estimate the original sources from the sensor array, without knowing the transmission channel characteristics. Besides methods based on independent component analysis which is one of the most powerful tools for blind source separation, several methods based on time-frequency analysis have been proposed. One of them is the quotient signal estimation method which can estimate the unknown number of sources. The notion of the continuous multiwavelet transform is introduced and three types of multiwavelets are presented. A new method using continuous multiwavelet transform, position-scale information matrices and self-organizing maps, is presented and applied to image source separations with noise. The performance of three multiwavelets are compared.

主催：文部科学省， 大阪教育大学 場所：大阪教育大学 天王寺キャンパス 日程：平成 23 年 9 月 12 日（月）13:00 - 18:00 平成 23 年 9 月 13 日（火） 9:00 - 12:30

Multiwavelets are derived from refutable function vectors via a multiresolution analysis. A new method for blind image source separation based on position-scale information denned by multiwavelets is presented. © 2011 IEEE.

The monogenic signal is a multidimensional generalization of the analytic signal. The monogenic signal of the continuous wavelet transform is called the monogenic wavelet transform. Stationary wavelet transform is a redundant discrete wavelet transform, which is translation-invariant. A new method for blind image source separation based on position-scale information using the monogenic wavelet transform discretized by the stationary wavelet transform is presented. © 2011 IEEE.

Blind Source Separation (BSS) is one of the hottest emerging areas in signal processing. The purpose of BSS is to separate and to estimate the original sources from the sensor array, without knowing the transmission channel characteristics. Besides methods based on independent component analysis which is one of the most powerful tools for BSS, several methods based on time-frequency analysis have been proposed. One of them is the quotient signal estimation method which can estimate the unknown number of sources. We present a new method using wavelet analysis and apply it to signal and image source separations.

The Riesz transforms and the monogenic signal are two-dimensional generalizations of the Hilbert transform and the analytic signal, respectively. The monogenic wavelet transformation is introduced and applied to solve blind source separation problems for images. The monogenic wavelet transformation is used to define spatial-scale information. Algorithms to solve the image separation problem are proposed. © 2010 IEEE.

The algorithms to solve the simplest version of spatio-temporal mixture problem in the blind source separation are proposed. The analytic wavelet transform is used to represent time-frequency information and a numerical simulation is given. ©2008 IEEE.

The blind source separation method for spatial mixing problem is discussed. A matrix called time-frequency information matrix is composed of continuous wavelet transforms of observed signals with respect to several different wavelet functions. The proposed method detects the number of sources and separates sources using the time-frequency information matrix. Algorithms axe given, and numerical experiments demonstrate the proposed method works well.

To treat the blind source separation problems, in many cases, either statistical independence or statistical orthogonality (uncorrelation) on the sources has been assumed. Napoletani-Berenstein-Krishnaprasad treated the problem under the linear independence of the windowed Fourier transforms of sources and the continuity of density functions defined statistically. In this paper, another independence of the windowed Fourier transforms of sources in a time-frequency domain is proposed without assuming any statistical conditions. This paper is a summary of the authors' submitted papers. (C) 2006 Elsevier Inc. All rights reserved.

The blind source separation problem is discussed in this article. Focusing on the assumption of independency of the sources in the time-frequency domain, we present a mathematical formulation for the estimation problem of the number of sources. The proposed method uses the quotient of complex valued time- frequency information of only two observed signals to detect the number of sources. No fewer number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. We propose algorithms with feedback and give numerical simulations to show the method works well even for noisy case.

The cocktail party problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noise. The blind source separation problem corresponds to a way to enable computers to solve the cocktail party problem in a satisfactory manner. A blind source separation based on time-frequency informations for spatio-temporal mixture problems is discussed.

CRM preprint http://www.crm.umontreal.ca/en/niveau2/index_pub.html

CRM preprint http://www.crm.umontreal.ca/en/niveau2/index_pub.html

査読無しの技術報告

査読無しの技術報告

Digital image compression with multiresolution singular value decomposition is compared with discrete cosine transform, discrete 9/7 biorthogonal wavelet transform, Karhunen-Loeve transform, and combinations thereof The coding methods used SPIHT and run-length with Huff-mann coding. The performances of these methods differ little from each other. Generally, the 9/7 biorthogonal wavelet transform is superior for most images that were tested for given compression rates. But for certain block transforms and certain images other methods are slightly superior. (c) 2005 Elsevier Ltd. All rights reserved.

CRM preprint http://www.crm.umontreal.ca/en/niveau2/index_pub.html

CRM preprint http://www.crm.umontreal.ca/en/niveau2/index_pub.html