• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.423-430
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• 2005

In this paper. a microphone array beamforming algorithm that reduces the signal distortion caused by reverberation and near-field effect in car environment is proposed. When reverberation or near-field effect is present, an optimum beamformer should be constructed with a steering vector consisting of transfer functions between source and microphones, but it is generally difficult to estimate transfer functions on-line without knowledge of the source signal. Instead, a sub-optimal beamforming algorithm that reduces signal distortion is proposed. It is constructed with steering vectors consisting of relative transfer functions between reference sensor and other sensors. In order to evaluate the performance of the proposed algorithm. we had recorded noisy speech database in a car, and performed speech recognition experiments with HMM Toolkit (HTK) released by Cambridge University. The recognition rate of the proposed algorithm was 15 percents higher than that of the conventional far-field beamformers in best case.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.115-122
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• 2011

In this paper, we present a method to estimate an accurate real-time sound source direction based on time delay of arrival by using generalized cross correlation with four cross-type microphones. In general, existing systems have two disadvantages such as system embedding limitation due to the necessity of data acquisition for signal processing from microphone input, and real-time processing difficulty because of the increased number of channels for sound direction estimation using DSP processors. To cope with these disadvantages, the system considered in this paper proposes hardware design for enhanced real-time processing using microphone array signal processing. An accurate direction estimation and its design time reduction is achieved by means of an efficient hardware design using spatial segmentation methods and verification techniques. Finally we develop a system which can be used for embedded systems using a sound codec and an FPGA chip. According to experimental results, the system gives much faster real-time processing time compared with either PC-based systems or the case with DSP processors.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.519-525
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• 2018

This paper deals with noise suppression of multi-channel data captured by microphone array using multi-channel Wiener filter. Multi-channel Wiener filter does not rely on information about the direction of the target speech and can be partitioned into an MVDR (Minimum Variance Distortionless Response) spatial filter and a single channel spectral filter. The acoustic transfer function between the single speech source and microphones can be estimated by subspace decomposition of multi-channel Wiener filter. The errors are incurred in the estimation of the acoustic transfer function due to the errors in the estimation of correlation matrices, which in turn results in speech distortion in the MVDR filter. To alleviate the speech distortion in the MVDR filter, diagonal loading is applied. In the experiments, database with seven microphones was used and MFCC distance was measured to demonstrate the effectiveness of the diagonal loading.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.393-407
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• 2020

In order to reduce the threat by illegal unmanned aerial vehicles, a tracking system based on sound was implemented. There are three main points to the drone acoustic tracking method. First, it scans the space through variable beam formation to find a sound source and records the sound using a microphone array. Second, it classifies it into a hidden Markov model (HMM) to find out whether the sound source exists or not, and finally, the sound source is In the case of a drone, a sound source recorded and stored as a tracking reference signal based on an adaptive beam pattern is used. The simulation was performed in both the ideal condition without background noise and interference sound and the non-ideal condition with background noise and interference sound, and evaluated the tracking performance of illegal drones. The drone tracking system designed the criteria for determining the presence or absence of a drone according to the improvement of the search distance performance according to the microphone array performance and the degree of sound pattern matching, and reflected in the design of the speech reading circuit.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.72-77
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• 2024

Recently, the necessity for sound source localization has grown significantly across various industrial sectors. Among the sound source localization methods, sound intensimetry has the advantage of having high accuracy even with a small microphone array. However, the increase in localization error at high Helmholtz numbers have been pointed out as a limitation of this method. The study proposes a method to compensate for the bias error of the measured sound intensity vector according to the Helmholtz numbers by applying deep learning. The method makes it possible to estimate the accurate direction of arrival of the source by applying a dense layer-based deep learning model that derives compensated sound intensity vectors when inputting the sound intensity vectors measured by a tetrahedral microphone array for the Helmholtz numbers. The model is verified based on simulation data for all sound source directions with 0.1 < kd < 3.0. One can find that the deep learning-based approach expands the measurement frequency range when implementing the sound intensimetry-based sound source localization method, also one can make it applicable to various microphone array sizes.

• Journal of the Korean Society for Railway
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• v.19 no.3
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• pp.271-279
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• 2016

Pantograph aerodynamic noise is a major cause of noise that occurs when a train is traveling at high speeds. In this study, in order to analyze the contribution of pantograph aerodynamic noise, real train tests using HEMU-430X were carried out. In order to analyze the frequency characteristic of the noise of the pantograph in an actual vehicle, a sound field visualization has been carried out using a 144-channel microphone array at train speeds of 350 and 400km/h. As a result, it was confirmed that the low frequency noise in the 250~400Hz bandwidth provides the main contribution to the pantograph noise. And, in order to estimate the noise contribution of the pantograph, the noise level difference between cases in which the pantograph is ascending and those in which it is descending were compared in single microphone experiments. The frequency analysis in the single microphone tests showed that the bands of 315~400Hz and 1000~1250Hz are the main frequency characteristics of pantograph noise. These results show quite good agreement with those of previous studies and with results of sound field visualization.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.111-118
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• 2005

In this paper, we employ the modulation technique for improving the characteristics of beamformer in the low frequencies and thus improving the overall noise reduction performance. In the 1-dimensional uniform linear microphone arrays, we can suppress the narrowband noise component using the delay-and-sum beamforming. But, for the wideband noise signal, the delay-and-sum beamformer does not work well for the reduction of low frequency component because the inter-element spacing is usually set to avoid spatial aliasing at high frequencies. Hence, the beamwidth is not uniform with respect to each frequency and it is usually wider at the low frequencies. In order to obtain the beamwidth independent of frequencies, subarray systems[1][2][3][4] and multi-beamforming[5] have been proposed. However these algorithms need large space and more microphones since they are based on the theory that the size of the array is proportional to the wavelength of the input signal. In the proposed beamformer, we reduce the low frequency noise by using modulation technique that does not need additional sensors or non-uniform spacing. More Precisely, the array signals are split into subbands, and the low frequency components are shifted to high frequencies by modulation and reduced by the delay-and-sum beamforming techniques with small size microphone array. Experimental results show that the proposed technique Provides better performance than the conventional ones, especially in the low frequency band.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.123-129
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• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.38-45
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• 2005

The noise reduction of the In-The-Ear (ITE) hearing aid (HA) can be achieved by arrays of microphones. Each of the right and the left ears was assumed to have two HA microphones. These arrays of HA microphones produce particular patterns of directivity by some time delay between two microphones. The directivity pattern geometrically increase the S/N ratio. The boundary element method (BEM) was used for the three dimensional simulation of the HA directivity pattern with the two pairs' microphone arrays. The separation between two microphones was fixed to 10 mm. The time delay between the two microphones was calculated to produce the most narrow directivity pattern in the fore front of the head. The variation of the time delay was examined in accordance with input frequencies. This numerical analysis may be then applied for the calculation of the time delay parameter of the digital hearing aid DSP chip.