• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1005-1017
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• 2013

Voice activity detection is very important process that voice activity separated form noisy speech signal for speech enhance. Over the past few years, many studies have been made on voice activity detection, but it has poor performance in low signal to noise ratio environment or fickle noise such as car noise. In this paper, it proposed new voice activity detection algorithm using ensemble variance based on wavelet band entropy and soft thresholding method. We conduct a survey in a lot of signal to noise ratio environment of car noise to evaluate performance of the proposed algorithm and confirmed performance of the proposed algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.283-287
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• 2012

Recently, Most of diverse noise of vehicles has decreased competitively according to development of the automotive manufacturing technology. Especially, Passenger car manufacturers has been conducting buzz, squeak and rattle(BSR) noise test as a method of the noise evaluation tests to reduce an unpleasant sound from interior parts on the driving the car. The purpose of this paper is to confirm the change of the noise generated in the product after the reliability evaluation. Here by the BSR test procedure used the test regulation of 'G' company.

• Speech Sciences
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• v.15 no.2
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• pp.67-77
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• 2008

In this paper, we implemented a robust speech recognizer using the TMS320VC33 DSP. For this implementation, we had built speech and noise database suitable for the recognizer using spectral subtraction method for noise removal. The recognizer has an explicit structure in aspect that a speech signal is enhanced through spectral subtraction before endpoints detection and feature extraction. This helps make the operation of the recognizer clear and build HMM models which give minimum model-mismatch. Since the recognizer was developed for the purpose of controlling car facilities and voice dialing, it has two recognition engines, speaker independent one for controlling car facilities and speaker dependent one for voice dialing. We adopted a conventional DTW algorithm for the latter and a continuous HMM for the former. Though various off-line recognition test, we made a selection of optimal conditions of several recognition parameters for a resource-limited embedded recognizer, which led to HMM models of the three mixtures per state. The car noise added speech database is enhanced using spectral subtraction before HMM parameter estimation for reducing model-mismatch caused by nonlinear distortion from spectral subtraction. The hardware module developed includes a microcontroller for host interface which processes the protocol between the DSP and a host.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.437-442
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• 2015

Voice activity detection is very important process that voice activity separated form noisy speech signal for speech enhance. Over the past few years, many studies have been made on voice activity detection, but it has poor performance in low signal to noise ratio environment or fickle noise such as car noise. In this paper, it proposed new voice activity detection algorithm using ensemble variance based on wavelet band entropy and soft thresholding method. We conduct a survey in a lot of signal to noise ratio environment of car noise to evaluate performance of the proposed algorithm and confirmed performance of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.75-80
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• 2010

Noise handing is very important in voice recognition of vehicle environment. that has been studying about to hardware and software approach. hardware method that is noise filter circuit design, basically using Low-pass filter. it was shown a good result. and the side of software that has been developing about to algorithm for Noise canceler, NN(neural network), etc. in this paper we have analysis about to classified parameter pattern matting level for voice recognition on car noise environment that use of DTW(Dynamic Time Warping) which is applicable time series pattern recognition algorithm.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.147-150
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• 2005

The performance of speech recognition in car environment is severely degraded when there is music or news coming from a radio or a CD player. Since reference signals are available from the audio unit in the car, it is possible to remove them with an adaptive filter. In this paper, we present experimental results of speech recognition in car environment using the echo canceller. For this, we generate test speech signals by adding music or news to the car noisy speech from Aurora2 DB. The HTK-based continuous HMT system is constructed for a recognition system. In addition, the MMSE-STSA method is used to the output of the echo canceller to remove the residual noise more.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.395-402
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• 2008

Road traffic noise causes considerable disturbance and annoyance in exposed inhabitants. Particularly, arterial road noise is a significant environmental problem in many urban areas in which higher traffic volume and higher car speed occur. Arterial road noise became the target of this investigation in Seoul, South Korea. Noise levels were measured at four points that were based on distance from roadside at the same measurement site and under the conditions as reported by the National Institute of Environmental Research (NIER) in 1999. The average noise levels ($L_{eq,1h}$) of the arterial road was 80.3 dBA at 5 m, 77.4 dBA at 10 m, 73.7 dBA at 20 m, 70.9 dBA at 30 m. A comparison between 1999 and 2008's measurement values has shown that in 2008 noise level is up by about 1.5 dBA, traffic volume has increased by about 15.7%, while car speed has decrease by about 8%. The relationship between 2008' measured values and predicted values using the NIER Equation is low under 10 m from the roadside. The influence range of arterial noise is calculated at 26 m for road noise limits in daytime. In relation to the comparison between traffic volume and noise level, the equivalence in traffic volume (Light car+10xHeavy car) is higher than other variables.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.28-32
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• 2009

This paper compares two speech estimators under a generalized Gamma distribution for DFT-based single-microphone speech enhancement methods. For the speech enhancement, the noise estimation based on recursive averaging spectral values by spectral minimum noise is applied to two speech estimators based on the generalized Gamma distribution using $\kappa$=1 or $\kappa$=2. The performance of two speech enhancement algorithms is measured by recognition accuracy of automatic speech recognition(ASR) in car noisy environment.

• Proceedings of the KSR Conference
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• 2000.11a
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• pp.157-164
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• 2000

This study is about predicting the interior pressure level of the korean high speed train using ray acoustic method. The motor car and the motor and passenger cabin are investigated under the environment of passing open countryside and inside tunnel of 350 km/hr. Calculated sound levels are compared with the proposed sound levels and suggestions about the transmission Joss values of isolating panels inside motor car and the guide lines of allowed sound power limit of motor equipments are provided. Results of TPI car show calculated interior sound level is below the proposed values for both cases of open countryside running and inside tunnel. Since ray acoustic method calculated only air borne noise component, real sound level of the motor car may be higher than prediction. Passenger cabins of TMI, TM5 show higher sound level than the proposed values, so window method was carried out to find the contribution of each panel components and point out the remedy of transmission path. Reduction of sound power of motor equipments should be condisered at the same time.