• The Journal of the Acoustical Society of Korea
• /
• v.22 no.8
• /
• pp.670-679
• /
• 2003

This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.15 no.11
• /
• pp.955-961
• /
• 1990

Test statistics are obtained for detection of weak signals in signal-dependent noise using rank statistics. A generalized model is used in this paper in order to consider non-additivenoise as well as purely-additive noise. Locally optimum rank detectors for the model are shown to have similarity to locally optimum detectors and to be generalizations of these for the purely-additive noise model. A similar result is obtained for multi-input cases.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.9
• /
• pp.977-983
• /
• 2014

A repeat-back jamming signal is an intentionally re-broadcasted GNSS (Global Navigation Satellite System) interference. In this paper, a novel repeat-back jamming detection scheme is proposed. The proposed scheme uses a combined pseudo random noise signal (C-PRN) and is available for a generic GNSS receiver with a single antenna. The C-PRN signal is made by combining several received pseudo random noise signals that had been transmitted from the visible GNSS satellites. Through a Monte-Carlo simulation, the detection probability of a repeat-back jamming signal detected with the proposed scheme is presented.

• Journal of Advanced Marine Engineering and Technology
• /
• v.32 no.5
• /
• pp.768-774
• /
• 2008

In many areas of speech signal processing such as automatic speech recognition and packet based voice communication technique, VAD (voice activity detection) plays an important role in the performance of the overall system. In this paper, we present a new feature parameter for VAD which is the product of energy of the signal and the difference of two types of entropies. For this end, we first define a Mel filter-bank based entropy and calculate its difference from the conventional entropy in frequency domain. The difference is then multiplied by the spectral energy of the signal to yield the final feature parameter which we call PEED (product of energy and entropy difference). Through experiments. we could verify that the proposed VAD parameter is more efficient than the conventional spectral entropy based parameter in various SNRs and noisy environments.

• Speech Sciences
• /
• v.7 no.3
• /
• pp.153-165
• /
• 2000

During the phonation of voiced sounds, instants exist where the glottis is opened or closed, due to the periodic vibration of the vocal cord. When closed, this is called the glottal closure instant(GCI) or epoch.. The correct detection of the GCI is one of the important problems in speech processing for pitch detection, pitch synchronous analysis, and so on. Recently, it has been shown that the local maxima points of the wavelet transformed speech signal correspond to the GCIs of speech signal. In this paper, we investigate the accuracy of Gels estimated from this wavelet transformed speech signal. For this purpose we compare them with the negative peak points of the differentiated EGG signal that represents the actual GCIs of speech signal.

• International Journal of Contents
• /
• v.3 no.2
• /
• pp.35-39
• /
• 2007

The cognitive radio communication is taking the attentions because the development of the technique came to be possible to analyze wireless signals. In the IEEE 802.22 WRAN Systems[1], how to detect a spectrum and signals is continuously studied. In this paper, we propose the efficient signal detection method using SCF (Spectral Correlation Function). It is easy to detect the signal feature when we are using the SCF. Because most modulated signals have the cyclo-stationarity which is unique for each signal. But the fading channel effected serious influence even though it detects the feature of the signal. We applied LMS(Least Mean Square) filter for the compensation of the signal which is effected the serious influence in the fading channel. And we analyze some signal patterns through the SCF. And we show the unique signal feature of each signal through the SCF method. It is robust for low SNR(Signal to Noise Ratio) environment and we can distinguish it in the fading channel using LMS Filter.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.1012-1014
• /
• 1997

This paper introduces wavelets and shows that they may be efficient and useful for the detection of general faults in power system. The wavelet transform of a signal consists in measuring the "similarity" between the signal and a set of translated and scaled versions of a "mother wavelet". The "mother wavelet" is a chosen fast decaying oscillation function. A number of mother wavelet for signal analysis have been proposed and some of them are in use in fault detection. However, the performance of fault detection depend on used mother wavelet. In the present paper a comparative evaluation of different mother wavelets for low impedance fault detection is performed. The discussion is focused in well-known mother wavelet based wavelet transform. Several families of wavelets are used to analyse transient earth fault signals in a 345kV model system as generated by EMTP.

• Structural Engineering and Mechanics
• /
• v.40 no.6
• /
• pp.745-762
• /
• 2011

Damage detection methodologies based on the direct examination of the nonlinear-nonstationary characteristics of the structure dynamic response may play an important role in online structural health monitoring applications. Different signal processing based damage detection methodologies have been proposed based on the uncovering of spikes in the high frequency component of the structural response obtained via Discrete Wavelet transforms, Hilbert-Huang transforms or high pass filtering. The performance of these approaches in systems subjected to different types of excitation is evaluated in this paper. It is found that in the case of random excitations, like earthquake accelerations, the effectiveness of such methodologies is limited. An alternative damage detection approach using the Continuous Wavelet Transform (CWT) is also evaluated to overcome this limitation. Using the CWT has the advantage that the central frequencies at which it operates can be defined by the user while the frequency bands of the detail functions obtained via DWT are predetermined by the sampling period of the signal.

• Journal of Navigation and Port Research
• /
• v.32 no.2
• /
• pp.133-140
• /
• 2008

In this paper, a fault detection method for operator failures using the observation technique is proposed. The suggested algorithm is extended using the conventional sensor/actuator fault detection method. First, it is assumed that operator failure affects human work operations, as it is an external input signal. With this assumption, a human work model with operator failure is suggested. Second, an unknown input observer with proportional and integral gains is introduced. The characteristic of this observer of estimating an external signal without an exact input is shown, and the conditions for the detection of an operator failure are proposed. Finally, by simulating the container crane operations, it is verified that the observer can accurately detect an operator failure and estimate its magnitude from the given internal signal.

• The Journal of the Acoustical Society of Korea
• /
• v.30 no.6
• /
• pp.353-359
• /
• 2011

This paper proposes a vocal enhancement technique for improving the performance of vocal pitch detection in polyphonic music signal. The proposed vocal enhancement technique predicts an accompaniment signal from the input signal and generates an accompaniment replica signal according to the vocal power. Then, it removes the accompaniment replica signal from the input signal, resulting in a vocal-enhanced signal. The performance of the proposed method was measured by applying the same vocal pitch extraction method to the original and the vocal-enhanced signal, and the vocal pitch detection accuracy was increased by 7.1 % point in average.