• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.109-116
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• 2010

In this paper, we propose a method for extracting predominant melody of polyphonic music based on harmonic structure. Since polyphonic music contains multiple sound sources, the process of melody detection consists of extraction of multiple fundamental frequencies and determination of predominant melody using those fundamental frequencies. Harmonic structure is an important feature parameter of monophonic signal that has spectral peaks at the integer multiples of its fundamental frequency. We extract all fundamental frequency candidates contained in the polyphonic signal by verifying the required condition of harmonic structure. Then, we combine those harmonic peaks corresponding to each extracted fundamental frequency and assign a rank to each after calculating its harmonic average energy. We finally run pitch tracking based on the rank of extracted fundamental frequency and continuity of fundamental frequency, and determine the predominant melody. We measure the performance of proposed method using ADC 2004 DB and 100 Korean pop songs in terms of MIREX 2005 evaluation metrics, and pitch accuracy of 90.42% is obtained.

• ETRI Journal
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• v.39 no.6
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2119-2128
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• 2016

Due to detection and control errors, some high-frequency harmonics with voltage-source characteristics cause circulating currents in modular inverters. Moreover, the circulating currents are usually affected by the output filters (OF) of each module due to their filter and resonance properties. The interaction among the circulating currents in the modules increase the power loss and reduce system stability and control precision. Therefore, this paper reports the results of a study on the SPWM high-frequency harmonics circulating currents for a double-module VSI. In the paper, an analysis of the circulating-current circuits is briefly described. Next, a mathematic model of the single-module output voltage based on the carrier frequency of SPWM is built. On this basis, through mathematic modeling of high-frequency harmonic circulating currents, the formation mechanism and distribution characteristics of circular currents and their influences are studied in detail. Finally, the influences of the OF on the circulating currents are studied by mainly taking an LC-type filter as an example. A theoretical analysis and experimental results demonstrate some important characteristics. First, the carrier phase shifting of the SPWM for each module is the major cause of the SPWM harmonic circulating currents, and the circulating currents are in an odd distribution around n-times the carrier frequency $n{\omega}_s$, where n = 1, 2, 3, ${\ldots}$. Second, the harmonic circular currents do not flow into the parallel system. Third, the OF can effectively suppress the non-circulating part of the high-frequency harmonic currents but is ineffective for the circulation part, and actually reduces system stability.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.169-174
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• 2010

This paper explains end-point detection method for better speech recognition rates. The proposed method determines speech and non-speech region with the entropy and the harmonic detection of speech. The end-point detection using entropy on the speech spectral energy has good performance at the high SNR(SNR 15dB) environments. At the low SNR environment(SNR 0dB), however, the threshold level of speech and noise varies, so the precise end-point detection is difficult. Therefore, this paper introduces the end-point detection methods which uses speech spectral entropy and harmonics. Experiment shows better performance than the conventional entropy methods.

• Journal of Power Electronics
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• v.16 no.2
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• pp.760-768
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• 2016

Three-phase grid-connected inverters are widely applied in the fields of new energy power generation, electric vehicles and so on. Islanding detection is necessary to ensure the stability and safety of such systems. In this paper, feature recognition technology is applied and a novel islanding detection method is proposed. It can identify the features of inverter systems. The theoretical values of these features are defined as codebooks. The difference between the actual value of a feature and the codebook is defined as the quantizing distortion. When islanding happens, the sum of the quantizing distortions exceeds the threshold value. Thus, islanding can be detected. The non-detection zone can be avoided by choosing reasonable features. To accelerate the speed of detection and to avoid miscalculation, an active islanding detection method based on feature recognition technology is given. Compared to the active frequency or phase drift methods, the proposed active method can reduce the distortion of grid-current when the inverter works normally. The principles of the islanding detection method based on the feature recognition technology and the improved active method are both analyzed in detail. An 18 kVA DSP-based three-phase inverter with the SVPWM control strategy has been established and tested. Simulation and experimental results verify the theoretical analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.127-136
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• 2013

The islanding detection and prevention function in the important performance of the grid-connected inverter is the essential details which should be considered for worker's safety to maintain the system. This paper tries to suggest the active frequency shake islanding detection technique in the grid-connected PV generation system using sunlight. The existing active frequency drift method algorithm is described to suggest active frequency shake method algorithm. And, the algorithm which can reduce the non-detection zone, improve THD, and improve the detection speed in comparison with the existing algorithm is suggested. And we applied the proposed algorithm to the 3kW grid-connected photovoltaic inverter generation and verified the feasibility of the technique through the simulation and experiments.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.510-514
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• 2011

To detect the insulator in the fault state on the electric poles, we first measured radiation sounds from normal state insulators and error state insulators in the anechoic chamber. We processed the signals in frequency domain to find the features with filter bank, narrow band and wide band analysis. So we could found two apparent results from their frequency spectrums - one was 120Hz harmonic components, the other was high average noise level than normal state ones. Then we also introduced a technique for the direction detection of the fault state insulator using the cross correlation from the three dimensional array microphones. To eliminate the noise signal from unexpected directions, we suggested the zero padding technique in cross correlation function. From these, we could conclude that acoustic fault detection techniques are useful of the detection of insulators' faults and the estimation of the direction of the fault state insulators.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.506-513
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• 2001

The islanding detection for distributed resources (DR) becomes an important and emerging issue in power system protection since the distributed generator installations are rapidly increasing and most of the installed systems are interconnected with distribution network. In order to avoid the negative impacts from islanding operations of DR on protection, operation and management of distribution system, it is necessary to effectively detect the islanding operations of DR and rapidly disconnect it from distribution network. Generally, it is difficult to detect islanding operation by monitoring only one system parameter This paper presents a new logic based islanding detection method for distributed resources(DR) which are interconnected with distribution network. The proposed method detects the islanding operation by monitoring four system parameter: voltage variation, phase displacement, frequency variation, and the variation of total harmonic distortion(THD) of current; therefore, it effectively detects island operation of DR unit operating in parallel with the distribution network. We also verified the efficiency of the proposed algorithm using the radial distribution network of IEEE 34 bus model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.46-50
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• 1997

Acoustic signals from the vehicle muffler has various kinds of noises. For control of noise from the vehicle muffler, the major part of noise to be contorted is that correlated with the revolution of the vehicle engine. For this reason the most efficient method for noise control is to use the extracted acoustic signals correlated with revolution as a controlled factor. Therebefore in this paper we developed and proofed an algorithm for efficient amplitude detection and phase detection related to the engine operating revolution from the vehicle muffler noise by orthogonality.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.1
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• pp.23-30
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• 2013

This paper proposes a parametric model for inter-turn fault detection in a fault-tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short-circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.