• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.356-362
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• 2009

In modern society, most people of the world are densely populated in cities so that the traffic sound has a very significant meaning. people tend to classify traffic sound as a noise pollution while they are likely to categorize most nature sound as positive. In this paper, we applied various forms of FFT filters into white noise belonged in nature sound to find frequency characteristics of white noise which preferred by people and confirm its correlation with nature sound. In addition, we conducted an analysis through the comparison of various traffic and nature sound waveforms and spectra. As a result of analysis, the traffic sound have characteristics which sound energy had concentrated on specific frequency bandwidth and point of time compared to nature sound. And we confirmed the fact that these characteristics had negative elements to which could affect to people. Lastly, by letting the subjects listen directly to both traffic and nature sound through brainwave experiment using electrode, the study measured the energy distribution of alpha waves and beta waves. As a result of experiments, it has been noted that urban sound created a noticeably larger amount of beta waves than nature sound; on the contrary, nature sound generated positive alpha waves. These results could directly confirm the negative effects of traffic sound and the positive effects of nature sound.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.3
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• pp.244-251
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• 2014

This paper describes a prototype mechanical white noise generator has a source level of more than 170.0 dB (re $1{\mu}Pa$ at 1 m) at the frequency range of 10 Hz to 100 kHz. The results of performance evaluation of the generator are as follows. The average source level of the generator measured by a step of $15^{\circ}$ in horizontal (0 to $360^{\circ}$, 25 points) was 185.2 (SD (standard deviation): 2.3) dB (re $1{\mu}Pa$ at 1 m). The maximum and minimum source levels were appeared at the frequency range of 2.5 to 5.0 kHz and around 100 kHz, respectively. The average source levels at $0^{\circ}$, $90^{\circ}$, $180^{\circ}$ and $270^{\circ}$ were 162.9 (SD: 10.6), 168.4 (SD: 10.0), 162.1 (SD: 9.1) and 166.5 (SD: 11.1) dB (re $1{\mu}Pa$ at 1 m). The average source level measured by a step of $30^{\circ}$ in vertical was 184.9 (SD: 2.2) dB (re $1{\mu}Pa$ at 1 m). The relative maximum variation width of the source levels in horizontal and in vertical measurement were less than 7.0 dB and 1.0 dB, respectively.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.301-305
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• 2007

In this paper, we propose a simple algorithm for detecting underwater transient signals on the fact that the frequency range of underwater transient signals is similar to audio frequency. For this, we use a preprocessing module of EVRC speech codec that is the standard speech codec of the mobile communications. If a signal is entered into EVRC noise suppression module, we can get some parameters such as the update flag, the energy of each channel, the noise suppressed signal, the energy of input signal, the energy of background noise, and the energy of enhanced signal. Therefore the energy of the enhanced signal that is normalized with the energy of the background noise is compared with the pre-defined detection threshold, and then we can detect the transient signal. And the detection threshold is updated using the previous value in the noisy period. The experimental result shows that the proposed algorithm has $0{\sim}4% error rate in the AWGN or the colored noise environment.