This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. Our study's goal is predicting earthquakes using EM radiation waves by detecting some anomalies. We have been measuring the Extremely Low Frequency (ELF) range EM radiation waves all over Japan. However, the recorded data contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. In this paper, we propose an efficient method of global signal elimination and enhancement local signals using Independent Component Analysis (ICA). We evaluated the effectiveness of this method.

This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.

In audio teleconference systems, speech quality may be degraded due to acoustic echo in the conference room. Echo control devices, such as echo cancellers, can be used to improve the speech quality. This paper describes the echo return loss required for acoustic echo controllers used in audio teleconferencing based on subjective assessment. In particular, far-end or talker echo is subjectively evaluated using a simulated audio teleconference system constructed in our laboratory. Test conditions are given as combinations of transmission delay and insertion loss. The subjective tests were also evaluated under three different reverberation times of echo path. The thresholds of detectable and/or objectionable echoes and mean opinion score in view of acoustic echo and reverberation in audio teleconferencing are clarified. From this, we can ascertain that an echo objection limit that 90% of the subjects consider to be permissible leads to a required echo return loss of more than 40 dB when the overall round-trip delay time Tord is 100 ms and the reverberation time Trev is 400 ms.

Footsteps, with different shoes of heels, sneakers, leathers or even bare footed, will appear in different grounds of concrete, wood, etc. If a footstep is discriminable, the application to various fields can be considered. In this paper, the feature extraction of a footstep is investigated. We focus on the recognizing a certain kind of footstep waveforms under the restricted condition. We propose a new methodology using the feature parameter such as the peak frequency set by the mel-cepstrum analysis, the walking intervals and the similarity of spectrum envelope. It is shown for personal identification that the performance of the proposed method is effective.

This paper describes a system that can enchance the speech quality degradation due to severe band limitation during speech transmission. We have already proposed a spectrum widening method that utilizes aliasing in sampling rate conversion and digital filtering for spectrum shaping. This paper proposes a new method that offers improved performance in terms of the spectrum distortion characteristics. Implementation procedures are clarified, and its performance is discussed. The proposed method can effectively enhance speech quality.

The tectonic activities that precede significant earthquakes release electromagnetic (EM) waves that can be used as earthquake precursors. We have been observing EM radiation in the ELF (extremely low frequency) band at about 40 observation stations in Japan for predicting significant earthquakes. The recorded signals contain, however, several noise components generated from the ionosphere, human activity, and so on. Most background noise in observed signal is attributed to lightning in the tropics. This paper proposes method based on PCA (principal component analysis) to extract signals from large data sets. The good performance of the proposed method is confirmed.

The design of a high speed self-routing network switch module is described. Clock distribution and timing design to achieve high-speed operation are considered. A 88-port self-routing Benes network switch prototype on multi-chip module is fabricated using 44-port space division switch LSIs. The switch module achieves a maximum measured clock frequency of 750MHz under switching operation. Resultant total throughput of the switch module is 12Gbit/s.

We have developed a signal processing method that is appropriate for detecting electromagnetic radiation due to earthquake activities. The radiation is usually accompanied by a background noise that is mainly caused by atmospheric discharges in the tropical regions. Data representing the seismic radiation is presented as sound via the concept of sonification. This is useful for immediately finding out anomalous seismic radiations, which are often followed by a disastrous earthquake, from the massive data collected from over forty observation stations. It is illustrated that the auditory display is valuable for future earthquake prediction systems.

An acoustic echo canceller that also cancels room noise is proposed. This system has an additive (noise reference) input port, and a noise canceller (NC) precedes the echo canceller (EC) in a cascade configuration. The adaptation control problem for the cascaded echo and noise canceller is solved by controlling the adaptation process to match the occurrence of intermittent speech/echo; the room noise is a stationary signal. A simulation shows that adaptation using the NLMS algorithm is very effective for the echo and noise cancellation. Sub-band cancelling techniques are utilized. Noise cancellation is realized with a lower band EC. Hardware is implemented and its performance evaluated through experiments under a real acoustic field. The combination of the EC with NC maintains excellent performance at all echo to room noise power ratios. It is shown that the proposed canceller overcomes the disadvantages traditionally associated with ECs and NSc.

This paper describes the background noise estimation technique of the tensor product expansion with absolute error (TPE-AE) to estimate multiple sources. The electroencephalogram (EEG) signal produced by the saccadic eye movement is adopted to analyze relationship between a brain function and a human activity. The electrooculogram (EOG) generated by eye movements yields significant problems for the EEG analysis. The denoising of EOG artifacts is important task to perform an accurate analysis. In this paper, the two types of TPE-AE are proposed to estimates EOG and other components in EEG during eye movement. One technique estimates two outer products using median filter based TPE-AE. The another technique uses a reference signal to separate the two sources. We show that the proposed method is effective to estimate and separate two sources in EEG.

A method for evaluating the degradation of subband adaptive digital filters (ADF) is presented. The performance of a simple ADF that uses critical sampling is mainly influenced by the subband filter bank's characteristics and the finite precision arithmetic operations used. This paper considers a two-channel mirror filter bank and a normalized least mean square algorithm with floating point arithmetic. The theoretical ERLE (Echo Return Loss Enhancement) and the theoretical relationships between the output error of the ADF and the circuit parameters considering finite precision A/D conversion and finite word length effects in floating point arithmetic operation are obtained using an equivalent noise model. Simulation results are found to be in good agreement to analytical values; the difference is only 3 to 5 dB.