• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.210-216
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• 2013

It is hard to listen to the music clearly in the presence of background noise. In this paper, a method that modifies the audio signal automatically to enhance the audio listening experience in adverse environment is proposed. Specifically, the method that amplifies the audio signal so that the perceived loudness of audio signal in each band becomes similar to that of the noiseless signal. The loudness perception model proposed by Moore et. al is utilized. Extending the previous work that is applied to speech reinforcement, the full band signal sampled at 48kHz is manipulated based on the loudness restoration principle. Moreover, based on the observation that the audio clarity is compromised even with loudness restored signal, a modification that intentionally boosts high frequency loudness more than lower band is also proposed. Experimental results showed that the proposed algorithm can enhance the audio listening experience in adverse environment.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.113-119
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• 2020

Motor noise is a major concern in order to improve perceptual feeling of car interior sound due to increased motor usage in passenger cars. The purpose of this study is to propose factors that can represent the acoustic performance of motor noise according to the change of load. To this end, at first, it is shown that power spectrum and total loudness are not fit for noise performance, and then, PN_B, partial loudness related to the brush friction component, and PN_R, partial loudness related to the torque ripple component are investigated as factors representing motor noise. The performance curve of motor noise using PN_B and PN_R is proposed to identify trends of motor noise according to the loads. The curve could be a guide for the noise control, the selection of motor, and the improvement of a system.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.147-155
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• 2018

In this paper, we tried to find out sound quality metrics to express discomfort of overload excavator noise and to develop sound quality indexes through multiple regression analysis by using them. For this purpose, the interior noise of cabin under overload condition was recorded for six excavator models with different noise properties and Jury test was carried out by PCM (Paired Comparison Method) and MEM (Magnitude Estimation Method). Jury test result with low consistency was classified into two groups with different preference tendencies by cluster analysis and multiple regression analysis was conducted in order to find out which sound quality metrics have significant effects on discomfort(low preference). As a result, we figured out that the sound quality metrics to express the discomfort were the partial loudness (= $PN_{10Bark}$) between 0 and 10 Bark in case of group1 and the difference between engine noise(= $dB_{EG}$) and hydraulic system noise ($dB_1$) in case of group2. Using the results of preference ranking and tendency analysis of PCM followed by the correlation analysis between PCM and MEM, the more reliable results were adopted by excluding the data with low consistency obtained from Jury test via MEM.