• Proceedings of the KSR Conference
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• 2006.11b
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• pp.144-151
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• 2006

Sound generated from a moving vehicle often carries information on the condition of vehicle, for example, whether it has faults or not, where the fault exists. The latter is possible especially by MFAH(moving frame acoustic holography) and beamforming method. MFAH is applicable to the sound source of pure tone or narrow band noise. For the beamforming method, we have to know what kind of wave the sound source radiates, for example, plane wave or spherical wave. That is, whether the above methods are applicable depends on the characteristics of sound source. To apply these methods to the fault detection, we have to know the characteristics of wave from faults. In this research, a machine diagnosis technique based on the above holographic approaches is introduced to find the position of faults. The signal due to faults is modeled based on the fact that the faults radiate impulsive noise, and analyzed in time and frequency domain. The way how MFAH and beamforming method can be used is introduced to find the position of source.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.453-454
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• 2010

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.187-190
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• 2002

In this paper, a speech enhancement system using microphone array with MMSE-STSA (Minimum Mean Square Error-Short Time Spectral Amplitude) estimator based post-processing is proposed. Speech enhancement is first carried out by conventional delay-and-sum beamforming (DSB). A new MMSE-STSA estimator is then obtained by refining MMSE-STSA estimators from each microphone, which is applied to the output of conventional DSB to obtain additional speech enhancement. Computer simulation for white and pink noises show that the proposed system is superior to other approaches.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.484-488
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• 2007

In this paper, the measurement system using the microphone array developed for analyzing cabin noise of the vehicle and its applications are discussed. The sensor is a three dimensional microphone array, the microphones and cameras are equipped on the rigid sphere. The cameras are used for acoustic visualization. As applications, the experiments in both reverberation chamber and anechoic chamber are discussed. These results show that this system is very useful to evaluate or improve the vehicle sound insulation performance.

• Journal of KSNVE
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• v.6 no.6
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• pp.835-842
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• 1996

To identify the locations and strengths of acoustic sources, one may use a microphone line array. Apparent advantage of the source identification method utilizing a line array is that it requires less measurement points than intensity method and holography. This method is based on the information of magnitude and phase difference between pressure signals at each microphone. Since those differences are dependent on the source model, we have to assume them such as plane, monopole, etc. In this paper the conventional source identification methods such as beamforming method and MUSIC method are briefly reviewed by modeling a source as plane and spherical wave, then a modified method is introduced. This can be applied to sound field which may by either coherent or incoherent. Typical simulations and experiment are performed to confirm this identification method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.701-708
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• 2003

This paper presents an acoustic source localization technique on 2D cavity model in flow using a phased microphone way. Investigation was performed on cavity flows of open and closed types. The source distributions on 2D cavity flow were investigated in anechoic open-jet wind tunnel. The array of microphones was placed outside the flow to measure the far field acoustic signals. The optimum sensor placement was decided by varying the relative location of the microphones to improve the spatial resolution. Pressure transducers were flush-mounted on the cavity surface to measure the near-filed pressures. It is shown that the propagated far field acoustic pressures are closely correlated to the near-field pressures. It is also shown that their spectral contents are affected by the cavity parameter L/D.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.45-53
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• 2010

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1
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• pp.30-35
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• 2006

In this paper. we Propose a frequency domain beamforming algorithm composed of inverse-filtering stages followed by a MVDR (Minimum-Variance Distortionless Response) beamformer or a GSC (Generalized Sidelobe Canceller). The proposed method is shown to require less complexity than the conventional RTF-MVDR and TF-GSC. respectively, and it is shown that the Proposed method is equivalent to the conventional RTF-MVDR and TF-GSC in optimum solution. In order to evaluate the performance of the Proposed method. speech recognition experiments are performed using the speech database recorded in a car. The Proposed method shows equal or slightly degraded Performance comparing to the conventional methods in terms of the speech recognition rate.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.713-717
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• 2014

This paper presents a beamforming technique for locating impulsive sound source. The conventional frequency-domain beamformer is advantageous for localizing noise sources for a certain frequency band of concern, but the existence of many frequency components in the wide-band spectrum of impulsive noise makes the beamforming image less clear. In contrast to a frequency-domain beamformer, it has been reported that a time-domain beamformer can be better suited for transient signals. Although both frequency- and time-domain beamformers produce the same result for the beamforming power, which is defined as the RMS value of its output, we can use alternative directional estimators such as the peak value and crest factor to enhance the performance of a time-domain beamformer. In this study, the performance of three different directional estimators, the peak, crest factor and RMS output values, are investigated and compared with the incoherent interfering noise embedded in multiple microphone signals. The proposed formula is verified via experiments in an anechoic chamber using a uniformly spaced linear array. The results show that the peak estimation of beamformer output determines the location with better spatial resolution and a lower side lobe level than crest factor and RMS estimation in noise free condition, but it is possible to accurately estimate the direction of the impulsive sound source using crest factor estimation in noisy environment with stationary interfering noise.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.38-46
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• 1998

In this paper, we propose a remote speech input device, a new method of user-friendly speech input in spontaneous speech recognition system. We focus the user friendliness on hands-free and microphone independence in speech recognition applications. Our method adopts two algorithms, the automatic speech detection and the microphone array delay-and-sum beamforming (DSBF)-based speech enhancement. The automatic speech detection algorithm is composed of two stages; the detection of speech and nonspeech using the pitch information for the detected speech portion candidate. The DSBF algorithm adopts the time domain cross-correlation method as its time delay estimation. In the performance evaluation, the speech detection algorithm shows within-200 ms start point accuracy of 93%, 99% under 15dB, 20dB, and 25dB signal-to-noise ratio (SNR) environments, respectively and those for the end point are 72%, 89%, and 93% for the corresponding environments, respectively. The classification of speech and nonspeech for the start point detected region of input signal is performed by the pitch information-base method. The percentages of correct classification for speech and nonspeech input are 99% and 90%, respectively. The eight microphone array-based speech enhancement using the DSBF algorithm shows the maximum SNR gaing of 6dB over a single microphone and the error reductin of more than 15% in the spontaneous speech recognition domain.