• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.1009-1012
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• 2004

Recently, the concern on sound qualify (SQ) is on the steep increase in the fields of vehicle and home appliance and over the fast few decades a considerable number of studies have been conducted on SQ evaluation. As a result, basic procedure for SQ evaluation has been already suggested. Although most interesting sounds have time-varying features, however, little is known about their SQ evaluation. The purpose of this study is to systematize a method for SQ evaluation of non-stationary sound. For this, various listening tests procedure for non-stationary sound is introduced and it is attempted to find out correlation between various SQ metrics and subjective data obtain from listening test. Booming of car interior noise in acceleration is used as an example and finally, representative value is obtained for the interesting sensation of non-stationary sound.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.503-508
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• 2001

In this paper, time-fiequency analysis and multi-dimensional spectral analysis methods are applied for source identification and diagnosis of non-stationary sound/vibration signals. Sound or vibration problems of general vehicle and agricultural machinary are under 500 Hz. So We used linearly increased chirp signals under 500 Hz. By checking the coherences on concerned time, fur time-variant non-stationary signals, this simulation it very well coincident to expected results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1172-1183
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• 2012

Aure's tonality was considered as the sound metrics for the expression of the tonality of gear whine sound in a previous research. It was failed to use the Aure's tonality as a sound metric for the tonal impression. Thus Aures's tonality, was developed for tonal impression in previous research. However, this metric did not express well the tonality of gear whine sound since the whine sound is a non-stationary signal with frequency modulation and amplitude modulation. In this study, the new method for the tonality evaluation for a non-stationary signal is presented. It is developed based on the prominence ratio, tonality impression function, and lower threshold level. It improves the accuracy and reliability of the sound quality index being used for the sound quality evaluation of the axle-gear whine sound.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1154-1159
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• 2006

In this paper, time-frequency analysis and multi-dimensional spectral analysis methods are applied to source identification and diagnostic of non-stationary sound vibration signals. By checking the coherences for concerned time, this simulation is very well coincident to expected results. The proposed method analyzes the signal instantaneously in both time and frequency domains. The MDSA (Multiple Dimensional Spectral Analysis) analyzes the signal in the plane of instantaneous time and instantaneous frequency at the same time. And it was verified by using the 1500cc passenger car which is accelerated from 70Hz to 95Hz in 4 seconds, the proposed method is effective in determining the vehicle diagnostic problems.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.692-697
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• 1994

In the actual sound environmental systems, it seems to be essentially difficult to exactly evaluate a whole probability distribution form of its response fluctuation, owing to various types of natural, social and human factors. Up to now, we very often reported two kinds of unified probability density expressions in the standard expansion from of Hermite and Laguerre type orthonormal series to generally evaluate non-Gaussian, non-linear correlation and/or non-stationary properties of the fluctuation phenomenon. However, in the real sound environment, there still remain many actual problems on the necessity of improving the above two standard type probability expressions for practical use. In this paper, first, a central point is focused on how to find a new probabilistic theory of practically evaluating the variety and complexity of the actual random fluctuations, especially through introducing some equivalence transformation toward two standard probability density expressions mentioned above in the expansion from of Hermite and Laguerre type orthonormal series. Then, the effectiveness of the proposed theory has been confirmed experimentally too by applying it to the actual problems on the response probability evaluation of various sound insulation systems in an acoustic room.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.189-195
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• 2003

This paper represents an identification procedure for leading mechanism of a car A/V system which is composed of a DC motor and a set of plastic gears. In addition, we studied dominant noise source of rattle noise generated by external forced vibration as a car drives. we made a dynamometer to produce stationary operation on loading mechanism of A/V system because noise generated by actual loading mechanism is non-stationary signal. operating the dynamometer setup at various motor speeds, sound pressure spectra are measured and the results are analyzed. its dominant noise source is also identified by using a sound Intensity technique. we made use of multi-dimensional spectral analysis to rind a dominant rattle noise. this method is so useful to eliminate coherence between vibration sources and helps us obtain coherent output spectrum of individual vibration source which make a rattle noise.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1040-1043
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• 2001

This paper describes the principle behind Crank Angle Analysis, as implemented by Bruel ＆ Kjaer in the Non-Stationary Spatial Transformation of Sound Fields (NS-STSF) system. The NS-STSF system combines a Time Domain Holography measurement on for example an engine with two simultaneously recorded Tacho signals. The Tacho signals provide the crankshaft angle and the RPM at the instant of each instantaneous output (snap-shot) from Time Domain Holography. As a result, the system allows precise analysis of the temporal and spatial relation between the acoustical emission (or the vibration pattern) and the mechanical events during an engine cycle. Some results from a measurement on a DaimlerChrysler engine are presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.541-546
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• 1999

The head-related transfer function (HRTF), which expresses the acoustic process from the sound source to the human ears in the free field, contains critical informations which the location of the source can be traced. It also makes it possible to realize multi-dimensional acoustic system that can approximately generate non-existing sound source. The use of non-individual, common HRTF brings performance degradation in localization ability such as front-back judgment error, elevation judgment error. In this paper, we have reduced the error on vertical plane by increasing the spectral notch level. The performance of the proposed method was Proved through subjective test that it is Possible to improve the ability to locate stationary/moving source.