• ETRI Journal
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• v.39 no.1
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• pp.21-29
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• 2017

This paper addresses the problem of unsupervised speech separation based on robust non-negative matrix factorization (RNMF) with ${\beta}$-divergence, when neither speech nor noise training data is available beforehand. We propose a robust version of non-negative matrix factorization, inspired by the recently developed sparse and low-rank decomposition, in which the data matrix is decomposed into the sum of a low-rank matrix and a sparse matrix. Efficient multiplicative update rules to minimize the ${\beta}$-divergence-based cost function are derived. A convolutional extension of the proposed algorithm is also proposed, which considers the time dependency of the non-negative noise bases. Experimental speech separation results show that the proposed convolutional RNMF successfully separates the repeating time-varying spectral structures from the magnitude spectrum of the mixture, and does so without any prior training.

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.280-287
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• 2003

The unsteady flow characteristics and the related noise of separated incompressible laminar boundary layer flows (Re$\sub$$\delta$/* = 614, 868, and 1,063) are numerically investigated. The characteristic lines of the wall pressure are examined to identify the primary noise source, related with the unsteady motion of the vortex at the reattachment point of the separation bubble. The generation and propagation of the vortex-induced noise in the separated laminar boundary layer are computed by the method of Computational Aero-Acoustics (CAA), and the effects of Reynolds number, Mach number and adverse pressure gradient strength are examined.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.4
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• pp.552-559
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• 1998

Conducted noise in power line contains both the common mode(CM) and differential mode(DM) noise. These two modes of noise are caused by different noise sources and paths. Therefore, CM/DM noise must be deal with individually in EMI filter. In this paper the technique to separate power line noise is presented, which can be used to measure both the CM and the DM noise from total generated noise. Also, noise-separator is designed and experimental results showed 30 dB above of separation performance in 10 kHz~10 MHz.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• MALSORI
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• no.48
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• pp.81-91
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• 2003

In this paper, we propose a speech recognition method based on independent component analysis (ICA) and spectral enhancement techniques. While ICA tris to separate speech signal from noisy speech using multiple channels, some noise remains by its algorithmic limitations. Spectral enhancement techniques can compensate for lack of ICA's signal separation ability. From the speech recognition experiments with instantaneous and convolved mixing environments, we show that the proposed approach gives much improved recognition accuracies than conventional methods.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.97-100
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• 2003

This paper addresses a method of convolutive source separation that based on SEONS (Second Order Nonstationary Source Separation) [1] that was originally developed for blind separation of instantaneous mixtures using nonstationarity. In order to tackle this problem, we transform the convolutive BSS problem into multiple short-term instantaneous problems in the frequency domain and separated the instantaneous mixtures in every frequency bin. Moreover, we also employ a H infinity filtering technique in order to reduce the sensor noise effect. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach and compare its performances with existing methods.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.7-12
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• 2000

A new speech enhancement algorithm for speech corrupted by slowly varying additive colored noise is suggested based on a state-space signal model. Due to the FIR structure and the unimportance of long-term past information, the receding horizon (RH) FIR filter known to be a best linear unbiased estimation (BLUE) filter is utilized in order to obtain noise-suppressed speech signal. As a special case of the colored noise problem, the suggested approach is generalized to perform the single blind signal separation of two speech signals. It is shown that the exact speech signal is obtained when an incoming speech signal is noise-free.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.3
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• pp.35-46
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• 1997

In order to satisfy customer's requirements of ride comfort and high performance, it is necessary for designers to fully understand vehicle aerodynamics and wind noise of newly produced cars because characteristics of flow and wind noise are heavily dependent on each other. In this study numerical and experimental study have been carried out to analyse the effect of flow characteristics at around of rear view mirror on wind noise and soiling on the front S/W. As a result, it's found that the spiral flow mear the front pillar is weakened and spreaded because rear view mirror obstructs the flow. It is also shown that there is abrupt change of gradient of separa- tion line, separation area, intensity of spiral flow and turbulent kinetic energy with varying shape of neck and housing of rear view mirror.

• Korean Journal of Applied Biomechanics
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• v.13 no.1
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• pp.23-38
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• 2003

The purpose of this study is to develop an improved marker auto-identification algorithm for reduce of data processing time through improve the efficiency of noise elimination and marker separation. The maker auto-identification algorithm was programming named KUMAS used Delphi language. For the study, various experiments were conducted for the verification of KUMAS. and compared two systems of established with the KUMAS. Four different motions - cycling, gait, rotation, and pendulum -, were selected and tested. Motions were filmed 30Hz frames rate per second. ${\chi}^2$ used for statistical analysis. Significant level were ${\alpha}=.05$. The test results were as follow. 1. Increased the success ratio of marker auto-identification. 2. The efficiency of marker auto-identification was remarkably improved through marker separation, noise elimination. 3. The marker auto-identification ability was improved in 2D-image plane include the 3D motion. 4. Significant different were found between KUMAS and B-SYS(established system) with non-input the artificial noise frames, input the artificial noise frames and total frames.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.520-521
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• 2013