• Title/Summary/Keyword: acoustic filter

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Submarine Layer Structure By Seismic Reflection Survey Between Geoje Island And Namhae Island (탄성파 탐사로 본 거제도 남해도간의 해저지층 구조)

  • Song, Moo-Young;Jo, Kyu Chang
    • 한국해양학회지
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    • v.13 no.1
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    • pp.1-8
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    • 1978
  • A seismic reflection Survey was carried out in the offshere area between Geoje Island and Namhae Island, utilizing the echosounder with the frequency 28KHz and thd Uniboom with the filter band 800∼2000Hz. The results show the submarine topography, sedimentary layer structure and the depth distribution of the base rock. The water depth of the sea in the survey area is less than 80m; up to 40m contour line the sea bottom surface has a slight dip(about 1/1000), while in the zone deeper than 40m the bottom topography has a irregular relief. The thickness of the whole sedimentary deposit is about 20∼70m and divided into 3 layers: Upper layer(A layer) with horizontal laminae, intermediate layer(B layer) with cross-bedding and groove structure, and lower layer(C layer) not showing any sedimentary structure on the seismic reflection profile. The surface of the base rock is deeper gradually in the south-eastern part of the survey area and extends to 140m depth. The vertical sediments sequences, composed of B layer and A layer, show the type of transgressive sequences. It is interpreted that B layer was formed at one period when the sea level was lower 40∼60 than the present and ince then, following the rising of the sea level, A layer was deposited.

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A method of the cross-talk cancellation for an sound reproduction of 5.1 channel speaker system (5.1 채널 스피커 시스템 음향재생을 위한 크로스토크 제거방법)

  • Lee, Soo-Jeong;Cho, Gab-Ken;Kim, Soon-Hyob
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.4 s.304
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    • pp.159-166
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    • 2005
  • This thesis deals with a method to deliver more realistic sound by cancelling the cross-talk which is inherent to the 5.1 channel speaker system. First, the cross-talk cancellation method that eliminates cross-talks on the path from left speaker to right ear and from right speaker to left ear is explained. Then the application and replaying method using the cross-talk cancellation explained here is introduced. The acoustical model for cross-talk cancellation is the free field model This model minimizes distortion of sound. Many experts also make studies on this model. I used the bark scale sound quality compensation based on psycho-acoustic. For the surround channels, band-limited sound quality compensation is performed in the frequency domain.

Low-noise Design Method of Small-size Underwater Acoustic System and Module (초소형 수중 음향 시스템 모듈의 저잡음 설계 방법)

  • Lee, Dong-Ho;Shin, Young-San;Song, Jin-Ho;Wee, Jae-Kyung;Lee, Jeong-Min;Seol, Jae-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.31 no.5
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    • pp.309-316
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    • 2012
  • This paper suggest configuration method of small-size system and design method of module using multi power. In particular module designed to focus on PDN(Power Distribution Network) and filter configuration method to improve the characteristics of noise sensitive analog IC. For the prevention of high-voltage noise, manufactured module is used the ground-isolated technique and via stitching, and is connected grounds with a series of filters. In this paper, so we analyzed PDN structure through the simulation using lumped modeling and confirmed through measurement. Simulation results, when connecting 4.7uH inductor, we made certain that noise of -7dB decreases much more than when it did not. And it was confirmed 12% less than Background Noise.

Quality Improvement of Karaoke Mode in SAOC using Cross Prediction based Vocal Estimation Method (교차 예측 기반의 보컬 추정 방법을 이용한 SAOC Karaoke 모드에서의 음질 향상 기법에 대한 연구)

  • Lee, Tung Chin;Park, Young-Cheol;Youn, Dae Hee
    • The Journal of the Acoustical Society of Korea
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    • v.32 no.3
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    • pp.227-236
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    • 2013
  • In this paper, we present a vocal suppression algorithm that can enhance the quality of music signal coded using Spatial Audio Object Coding (SAOC) in Karaoke mode. The residual vocal component in the coded music signal is estimated by using a cross prediction method in which the music signal coded in Karaoke mode is used as the primary input and the vocal signal coded in Solo mode is used as a reference. However, the signals are extracted from the same downmix signal and highly correlated, so that the music signal can be severely damaged by the cross prediction. To prevent this, a psycho-acoustic disturbance rule is proposed, in which the level of disturbance to the reference input of the cross prediction filter is adapted according to the auditory masking property. Objective and subjective test were performed and the results confirm that the proposed algorithm offers improved quality.

KTX Interior Noise Reduction Performance Comparison Using Multichannel Active Noise Control for Each Section (다중채널 능동소음제어기법을 이용한 KTX 실내소음의 구간별 저감성능 비교)

  • Jang, Hyeon-Seok;Kim, Young-Ming;Lee, Tae-Oh;Lee, Kwon-Soon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.1
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    • pp.179-185
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    • 2012
  • Since the eco-era is getting closer, the importance of noise reducing in the passenger cars of high-speed train is very important. The active noise control is best choice to reduce low frequency noise because the passive one is too heavy for high speed trains where weight is so critical. Also ANC is able to reduce the ambient noise when the environmental-factor changes. To reduce a three-dimensional closed-space sound field like a car of a high-speed rail is hard to do using single channel ANC control system. We used multi-channel FXLMS algorithm which calculation speed is fast and the secondary path estimation is possible in order to take into account the physical delay in electro acoustic hardware control loudspeaker and power amplifier. Firstly, we have measured interior noise of KTX and estimated noise path in KTX test-bed. However there was some problem related to algorithm divergence and increasing the filter order. We have made a simulation of interior environment of KTX car by using three frequency bands of 120Hz, 280Hz, 360Hz as the most important for KTX ANC system. During this research the interior noise reduction of KTX car was made by using the multi-channel FXLMS algorithm. Reduction performance was evaluated and compared each other for open space section and tunnel section. in-situ experiment for the KTX noise reduction by proposed ANC was performed based on data obtained in simulation and they were compared for open space section and tunnel section as well.

A Study on the Bed Load Collision Sound Analysis Using Sound Sensor and Denoising Filter (음향센서와 디노이징 필터를 활용한 향상된 소류사 충돌음 분석 연구)

  • Kim, Sung Uk;Jun, Kye Won
    • Journal of Korean Society of Disaster and Security
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    • v.14 no.2
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    • pp.43-50
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    • 2021
  • In Korea, the frequency of soil disasters has soared recently due to increased torrential rains caused by abnormal weather conditions. In particular, soil generated from mountainous areas is flowing into small rivers along valleys, depositing rivers and adding to flood damage. In order to prevent damage from such soil disasters, it is important to predict sediments and to quantitatively identify bed load. In this work, we conducted an experiment to indirectly measure acoustic sensor-based bed load collision sounds using pipe hydrophones, and compared them with raw data by applying denoising methods to improve the reliability of the measured data. As a result, we derive results in a more clear analysis of bed load estimation by correcting noise when the denoising method is applied to raw data.

Blind Noise Separation Method of Convolutive Mixed Signals (컨볼루션 혼합신호의 암묵 잡음분리방법)

  • Lee, Haeng-Woo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.3
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    • pp.409-416
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    • 2022
  • This paper relates to the blind noise separation method of time-delayed convolutive mixed signals. Since the mixed model of acoustic signals in a closed space is multi-channel, a convolutive blind signal separation method is applied and time-delayed data samples of the two microphone input signals is used. For signal separation, the mixing coefficient is calculated using an inverse model rather than directly calculating the separation coefficient, and the coefficient update is performed by repeated calculations based on secondary statistical properties to estimate the speech signal. Many simulations were performed to verify the performance of the proposed blind signal separation. As a result of the simulation, noise separation using this method operates safely regardless of convolutive mixing, and PESQ is improved by 0.3 points compared to the general adaptive FIR filter structure.

Application of ray-based blind deconvolution to long-range acoustic communication in deep water (음선 기반 블라인드 디컨볼루션의 장거리 심해 환경으로의 적용)

  • Kim, Donghyeon;Park, Heejin;Kim, J.S.;Hahn, Joo Young
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.2
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    • pp.242-253
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    • 2022
  • When the source waveform is unknown, the Green's function can be estimated by Ray-based Blind Deconvolution (RBD) based on the simple array signal processing. In previous papers, RBD was successfully demonstrated using simulation and experiments in shallow water environment. In this paper, we investigate the applicability of RBD for a long-range communication (e.g., 30 km, 60 km, and 90 km) in a deep water environment (1,000 m ~), using experimental data conducted in the east of Pohang, South Korea, in October 2018. Data results are presented to demonstrate Green's function estimation of a communication signal (2.2 kHz ~ 2.9 kHz) using a 16-element, 42-m long vertical array. The results show that the Green's function estimated from RBD is comparable to that of matched filter result. Additional communication performance at a maximum range of 90 km will be also presented.

Optimizing Wavelet in Noise Canceler by Deep Learning Based on DWT (DWT 기반 딥러닝 잡음소거기에서 웨이블릿 최적화)

  • Won-Seog Jeong;Haeng-Woo Lee
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.1
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    • pp.113-118
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    • 2024
  • In this paper, we propose an optimal wavelet in a system for canceling background noise of acoustic signals. This system performed Discrete Wavelet Transform(DWT) instead of the existing Short Time Fourier Transform(STFT) and then improved noise cancellation performance through a deep learning process. DWT functions as a multi-resolution band-pass filter and obtains transformation parameters by time-shifting the parent wavelet at each level and using several wavelets whose sizes are scaled. Here, the noise cancellation performance of several wavelets was tested to select the most suitable mother wavelet for analyzing the speech. In this study, to verify the performance of the noise cancellation system for various wavelets, a simulation program using Tensorflow and Keras libraries was created and simulation experiments were performed for the four most commonly used wavelets. As a result of the experiment, the case of using Haar or Daubechies wavelets showed the best noise cancellation performance, and the mean square error(MSE) was significantly improved compared to the case of using other wavelets.

Voice Activity Detection Method Using Psycho-Acoustic Model Based on Speech Energy Maximization in Noisy Environments (잡음 환경에서 심리음향모델 기반 음성 에너지 최대화를 이용한 음성 검출 방법)

  • Choi, Gab-Keun;Kim, Soon-Hyob
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.5
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    • pp.447-453
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    • 2009
  • This paper introduces the method for detect voices and exact end point at low SNR by maximizing voice energy. Conventional VAD (Voice Activity Detection) algorithm estimates noise level so it tends to detect the end point inaccurately. Moreover, because it uses relatively long analysis range for reflecting temporal change of noise, computing load too high for application. In this paper, the SEM-VAD (Speech Energy Maximization-Voice Activity Detection) method which uses psycho-acoustical bark scale filter banks to maximize voice energy within frames is introduced. Stable threshold values are obtained at various noise environments (SNR 15 dB, 10 dB, 5 dB, 0 dB). At the test for voice detection in car noisy environment, PHR (Pause Hit Rate) was 100%accurate at every noise environment, and FAR (False Alarm Rate) shows 0% at SNR15 dB and 10 dB, 5.6% at SNR5 dB and 9.5% at SNR0 dB.