• Korean Journal of Remote Sensing
• /
• v.25 no.3
• /
• pp.215-223
• /
• 2009

The noise removal as a preprocessing was tried with various kinds of wavelet pairs. Wavelet transform for 2D images generally uses the same wavelets as basis functions in horizontal and vertical directions. A method with different wavelets was tried for each direction separately, which gives more precise interpretation of the classification. Total 486 pairs of wavelets from nine basis functions were tried to remove image noises. The classification accuracies before and after the noise removal were compared. Although all kinds of wavelet pairs showed the increased accuracies in classification, there were best and worst wavelet pairs depending on the data sets. Wavelet pairs with low energy percentage of LL band showed the high classification accuracy. A pattern was found in the results that very similar vertical accuracy was distributed for each horizontal ones. Since Haar is the shortest length filter, Haar could be a predictor wavelet to find the good wavelet pairs.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.666-669
• /
• 2005

Social noise survey and auditory experiment on residential noises such as floor impact, air-borne, bathroom, drainage and traffic noises were conducted to classily a noise insulation Performance in apartment building. The survey results showed that annoyance among subjective responses to residential noises was most greatly affecting to satisfaction with noises. In the survey, boundary limit between satisfaction and dissatisfaction was also determined. Auditory experiments was also undertaken to determine noise insulation performance according to the percent of satisfaction for individual noise source. Result of auditory experiment showed that the noise insulation performance for floor impact, airborne, drainage and traffic noise corresponding to 40 % satisfaction is 49 dB (L$_{i,Fmax,AW}$), 48 dB (R'w), N-41, and N-40, respectively. Finally, classes of noise insulation performance in apartment building were proposed according to satisfaction with noises

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.6
• /
• pp.994-1003
• /
• 1994

In this paper, a scheme of noise cancellation using spectral subreaction method with single input in an autombile noise environment is proposed. In order to remove the changing automonile noise components form the noisy speech signal, the noise of various states is analyzed and its characteristics are presented. For the decision of speech/nonspeech and the estimation of noise spectrum, a classification function is proposed on the basis of noise analysis. This function presents the precise decision of speech/nonspeech and the optimal estimation of noise spectrum with less computation. As the result of the estimation of noise spectrum by the proposed classification function, the clean speech signal is extracted from the noisy speech signal with high signal-to-ratio.

• Journal of KIISE:Software and Applications
• /
• v.28 no.9
• /
• pp.629-633
• /
• 2001

In this paper, we improve noise tolerance in image classification by combining ICA(Independent Component Analysis) with Normalization. When we add noise to the raw image data the degree of noise tolerance becomes N(0, 0.4) for PCA and N(0, 0.53) for ICA. However, when we use the preprocessing approach the degree of noise tolerance after Normalization becomes N(0, 0.75), which shows the improvement of noise tolerance in classification.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11a
• /
• pp.352.1-352
• /
• 2002

Auditory experiments based on subjective responses were undertaken for the heavy and light weight impact noises, rubber ball impact noise and real impact noise. Relations between noise levels and subjective evaluations were also investigated. As a result, it was found that the subjective responses of all floor impact noise sources showed a similar trend except real impact noise. The noise class was rated with the range of sensible satisfaction by investigating the various social responses for the floor impact noise. (omitted)

• Journal of the Korean Society for Railway
• /
• v.10 no.5
• /
• pp.480-486
• /
• 2007

The subway is a necessary public transportation in big cities, which many citizens are using now. However, the demands for subway inner circumstance by citizens are growing recently. Among them, the noise problem is the hot issue to be solved. So, in this study we classified the characteristics of subway noise using the classification and regression trees (CART) based on noise level data in line No. 5 in Seoul. After that We developed the models for effect of subway noise and analyzed the characteristics through it. The result of this study is that we need to consider the type of geometry design and operational factors when the problem of subway noise improves, because the factors which weigh with subway noise are different by type of geometry and operational part.

• Journal of Broadcast Engineering
• /
• v.20 no.1
• /
• pp.164-170
• /
• 2015

This paper deals with the adaptation of classification model in the binary mask approach to suppress noise in the noisy environment. The binary mask estimation approach is known to improve speech intelligibility of noisy speech. However, the same type of noisy data for the test data should be included in the training data for building the classification model of binary mask estimation. The eigenvoice adaptation is applied to the noise-independent classification model and the adapted model is used as noise-dependent model. The results are reported in Hit rates and False alarm rates. The experimental results confirmed that the accuracy of classification is improved as the number of adaptation sentences increases.

• ETRI Journal
• /
• v.46 no.4
• /
• pp.619-632
• /
• 2024

Medical signal processing requires noise and interference-free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two-level transpose Vedic multiplier. The proposed architecture identifies the zero-noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero-noise segregation. This is performed independently for the zero- and nonzero-noise intervals. Finally, the finite impulse response is estimated as the sum of zero- and nonzero-noise inputs at any finite classification.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.4
• /
• pp.374-379
• /
• 2002

In this paper, a nobel method to reduce noise using voice/unvoice classification is proposed. Voice and unvoice are an important feature of speech and the proposed method processes noisy speech differently for each voice/unvoice part. Speech is classified into voice/unvoice using zero-crossing rate and energy, and a modified speech/noise dominant-decision is proposed based on voice/unvoice classification. The proposed method was tested on conditions of white noise and airplane noise, and on the basis of comparing segmental SNR with the existing method and listening to the enhanced speech, a performance of the proposed method was superior to that of the existing method.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.6
• /
• pp.469-474
• /
• 2018

As urban population increases, research on urban environmental noise is getting more attention. In this study, we classify the abnormal noise occurring in traffic situation by using a deep learning algorithm which shows high performance in recent environmental noise classification studies. Specifically, we classify the four classes of tire skidding sounds, car crash sounds, car horn sounds, and normal sounds using convolutional neural networks. In addition, we add three environmental noises, including rain, wind and crowd noises, to our training data so that the classification model is more robust in real traffic situation with environmental noises. Experimental results show that the proposed traffic sound classification model achieves better performance than the existing algorithms, particularly under harsh conditions with environmental noises.