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