• Journal of the Korean Society for Railway
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• v.15 no.1
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• pp.23-28
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• 2012

In this paper, noise of Korean high-speed trains (KTX) running at different speed from 150 to 300km/h was measured by a microphone array system. From the measurement, relation between maximum sound pressure levels and train moving speeds of KTX was drawn and a regression coefficient from the relation was also derived. Moreover, increases of SPL with speeds of KTX were analyzed in the frequency domain. From the analysis, sound characteristics of passing-by noise of KTX were provided. Then, dominant noise source areas were obtained from the measurements and propagation patterns of KTX in vertical direction were also investigated. Finally, noise sources of KTX were identified from inspection of noise maps.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.358-367
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• 2016

SSL (Sound Source Localization) has been applied to several applications such as man-machine interface, video conference system, smart car and so on. But in the process of sound source localization, angle estimation error is occurred mainly due to the non-linear characteristics of the sine inverse function. So an approach was proposed to decrease the effect of this non-linear characteristics, which divides the microphone's covering space into narrow regions. In this paper, we proposed an optimal space dividing way according to the pattern of microphone array. In addition, sound source's 2-dimensional position is estimated in order to evaluate the performance of this dividing method. In the experiment, GCC-PHAT (Generalized Cross Correlation PHAse Transform) method that is known to be robust with noisy environments is adopted and triangular pattern of 3 microphones and rectangular pattern of 4 microphones are tested with 100 speech data respectively. The experimental results show that triangular pattern can't estimate the correct position due to the lower space area resolution, but performance of rectangular pattern is dramatically improved with correct estimation rate of 67 %.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.22-28
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• 2001

This paper describes the implementation of a robust speaker position location system using the voice signal received by microphone array. To be robust to the reverberation which is the major factor of the performance degradation, low-frequency phase restoration algorithm which eliminates the influence of reverberations using the low-frequency information of the CPSP function is proposed. The implemented real-time system consists of a general purpose DSP (TMS320C31 of Texas instruments), analog part which contains amplifiers and filters, and digital part which is composed of the external memory and 12-bit A/D converter. In the real conference room environment, the implemented system that was constructed by the proposed algorithms showed better performance than the conventional system. The error of the TDOA estimation reduced more than 15 samples.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.102-105
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• 2000

This paper propose new sound localization algorithm that calculates TDOA(Time Difference Of Arrival) between the two received signals via two microphone array, The proposed Subband CPSP is a development of Previous CPSP method using subband approach. It first split the received microphone signals into three frequency bands and then calculates subband CPSP with corresponding SNR weights. This type of algorithm, Subband CPSP, can provide more accurate TDOA estimation results because it limits the effects of environmental noise within each subband. To verify the performance of the proposed Subband CPSP algorithm, computer simulation was conducted and it was compared with previous CPSP method. From the both simulation results, the proposed Subband CPSP is superior to previous CPSP algorithm more than accuracy for TDOA estimation.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4E
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• pp.52-60
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• 2001

This paper describes real time implementation of subband sound localization system on a floating-point DSP TI TMS320C31. The system determines two dimensional location of an active speaker in a closed room environment with real noise presents. The system consists of an two microphone array connected to TI DSP hosted by PC. The implemented sound localization algorithm is Subband CPSP which is an improved version of traditional CPSP (Cross-Power Spectrum Phase) method. The algorithm first split the input speech signal into arbitrary number of subband using subband filter banks and calculate the CPSP in each subband. It then averages out the CPSP results on each subband and compute a source location estimate. The proposed algorithm has an advantage over CPSP such that it minimize the overall estimation error in source location by limiting the specific band dominant noise to that subband. As a result, it makes possible to set up a robust real time sound localization system. For real time simulation, the input speech is captured using two microphone and digitized by the DSP at sampling rate 8192 hz, 16 bit/sample. The source location is then estimated at once per second to satisfy real-time computational constraints. The performance of the proposed system is confirmed by several real time simulation of the speech at a distance of 1m, 2m, 3m with various speech source locations and it shows over 5% accuracy improvement for the source location estimation.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.609-612
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• 2000

본 논문에서는 음원에 관한 정보가 미지의 상황에서 마이크로폰 어레이를 사용하여 두 음원신호를 분리하는 ,시스템을 제안한다 이 시스템은 두 단계로 구성되어 있으며, 첫 번째 단계에서는 파워가 큰 제 1음원의 DOA(Direction Of Arrival)를 추정하고, AMUSE(Algorithm for Multiple Unknown Signals Extraction)법을 사용한 Blind Deconvolution에 의해 음원신호의 분리를 행한다 두 번째 단계에서는 파워가 낮은 제 2음원의 강조신호를 사용하여 DSA(Delay and Sum Array)법에 의해 제 2음원의 DOA를 추정하고,AMUSE법의 출력신호와 두 음원의 DOA를 이용하여 ANF(Adaptive Notch Filter)를 구성하고, 두 음원신호의 재 분리를 행한다. 그리고, 시뮬레이션을 통해 제안한 방법의 유효성을 검토한 결과 두 음원 신호가 분리 가능한 것이 확인되었다.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.4
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• pp.185-194
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• 2013

Abstract- In distant-talking environments, speech recognition performance degrades significantly due to noise and reverberation. Recent work of Michael L. Selzer shows that in microphone array speech recognition, the word error rate can be significantly reduced by adapting the beamformer weights to generate a sequence of features which maximizes the likelihood of the correct hypothesis. In this approach, called Likelihood Maximizing Beamforming algorithm (Limabeam), one of the method to implement this Limabeam is an UnSupervised Limabeam(USL) that can improve recognition performance in any situation of environment. From our investigation for this USL, we could see that because the performance of optimization depends strongly on the transcription output of the first recognition step, the output become unstable and this may lead lower performance. In order to improve recognition performance of USL, some post-filter techniques can be employed to obtain more correct transcription output of the first step. In this work, as a post-filtering technique for first recognition step of USL, we propose to add a Wiener-Filter combined with Feature Weighted Malahanobis Distance to improve recognition performance. We also suggest an alternative way to implement Limabeam algorithm for Hidden Markov Network (HM-Net) speech recognizer for efficient implementation. Speech recognition experiments performed in real distant-talking environment confirm the efficacy of Limabeam algorithm in HM-Net speech recognition system and also confirm the improved performance by the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.206-214
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• 2018

This paper proposes an impact point estimation algorithm of the rifle using microphone sensors. The proposed algorithm resolves the time synchronization problem by expanding the existing ToA (Time of Arrival) method to TDoA (Time Difference of Arrival) method and verifies the performance of the algorithm through the actual shooting experiments. By comparing analysis of the actual and the estimated impact points by the algorithm, it is confirmed that the proposed algorithm has excellent performance by estimating the impact point accurately within the tolerance range.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1993-2001
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• 2006

The piezoelectric bimorph film, which, as an actuator, can generate more effective displacement than the usual PVDF film, is used to control the turbulent boundary-layer flow. The change of wall pressures inside the turbulent boundary layer is observed by using the multi-channel microphone array flush-mounted on the surface when actuation at the non-dimensional frequency $f_b^+$:=0.008 and 0.028 is applied to the turbulent boundary layer. The wall pressure characteristics by the actuation to produce local displacement are more dominantly influenced by the size of the actuator module than the actuation frequency. The movement of large-scale turbulent structures to the upper layer is found to be the main mechanism of the reduction in the wall- pressure energy spectrum when the 700$700{\nu}/u_{\tau}$-long bimorph film is periodically actuated at the non- dimensional frequency $f_b^+$:=0.008 and 0.028. The biomorph actuator is triggered with the time delay for the active forcing at a single frequency when a 1/8' pressure-type, pin-holed microphone sensor detects the large-amplitude pressure event by the turbulent spot. The wall-pressure energy in the late-transitional boundary layer is partially reduced near the convection wavenumber by the open-loop control based on the large amplitude event.

• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.