• Title/Summary/Keyword: Acoustic feature

Search Result 238, Processing Time 0.021 seconds

Acoustic Signal Analysis for Exploration of Buried Objects in the Ocean (해저매몰체 탐사를 위한 음향신호의 분석)

  • Kim, Jin-Hoo;Han, Kun-Mo;Park, Jong-Nam
    • Journal of Ocean Engineering and Technology
    • /
    • v.9 no.2
    • /
    • pp.167-174
    • /
    • 1995
  • The anomlous signal, anomaly, recorded by a sub-bottem profiler is analized for exploration of buried objects in the ocean, This anomaly is known as a signal diffracted from the edge of the buried object. Signals obtained from model that and numerical simulation are analized for investigating characteristics of the diffracted signal. From this study a diffracted signal and a non-diffracted signal can be identified, and the location of the object can be obtained. In order to identify an object in the seafloor the dimension of the object should be greater than the wave length used for exploration, and the acoustic impedance should be much greater than that of sediments. A 2-trace stacking of the signals can enhance the feature of strongly diffracted signals whereas it can diminish weak signals.

  • PDF

Class-Based Histogram Equalization for Robust Speech Recognition

  • Suh, Young-Joo;Kim, Hoi-Rin
    • ETRI Journal
    • /
    • v.28 no.4
    • /
    • pp.502-505
    • /
    • 2006
  • A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

  • PDF

Hydraulic Pulsation and Noise Reduction using the Helmholtz Attenuator (헬름홀츠 감쇠기를 응용한 유압시스템의 유압맥동 및 소음 최소화 연구)

  • 김동현;이대옥;최근국
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • 1997.04a
    • /
    • pp.614-619
    • /
    • 1997
  • The hydraulic pressure pulsation has on the effected on the acoustic nosie and control performance of the hydraulic-servo system. The Helmholtz attenuator introduction on the hydraulic line is an efficient device to reduce the hydraulic pulsation. The salient feature of causing hydraulic pulsation and the frequency characteristics of Helmholtz attenuator are studied. The hydraulic filter design parameters such as the locating position, connecting orifice area and accumulator volume are mathematically analyzed. The instrumental works are carried out with the remarkable reduction of the hydraulic pressure pulsation magnitude and the acoustic noise level.

  • PDF

Fault Diagnosis of a Pump Using Analysis of Noise (작동음의 분석을 이용한 펌프의 고장진단)

  • 박순재;이신영
    • Transactions of the Korean Society of Machine Tool Engineers
    • /
    • v.12 no.6
    • /
    • pp.22-28
    • /
    • 2003
  • We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We obtained noises by a microphone, analysed and compared the signals converted to Sequency range for normal products, artificially deformed products. We tried to search a change of noise signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method for a detection of machine malfunction or fault diagnosis.

Fault Diagnosis of a Pump Using Analysis of Noise (작동음의 분석을 이용한 펌프의 고장진단)

  • 박순재;이신영
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 2003.04a
    • /
    • pp.99-104
    • /
    • 2003
  • We should maintain the minimum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We obtained noises by a microphone, analysed and compared the signals converted to frequency range for normal products, artificially deformed products. We tried to search a change of noise signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method for a detection of machine malfunction or fault diagnosis.

  • PDF

The Abnormal Condition Diagnosis of Compressor Parts using Multi-signal Sensing (복합신호 검출에 의한 압축기 부품의 상태 진단)

  • Lee, Kam-Gyu;Kim, Jeon-Ha;Kang, Ik-Su;Kang, Myung-Chang;Kim, Jeong-Suk
    • Journal of the Korean Society of Manufacturing Process Engineers
    • /
    • v.3 no.3
    • /
    • pp.11-16
    • /
    • 2004
  • In this study, the characteristics of signals such as acoustic emission, vibration amplitude and noise level which are derived from the abnormal condition of compressor are investigated. The normal condition, vane stick sound and roller defect condition are chosen to analyze the signal in each cases. From the feature extraction of each signals, the dominant parameters of each signals which can identify the abnormal condition are suggested.

  • PDF

Classifying Seafloor Sediments Using a Probabilistic Neural Network (확률 신경망에 의한 해저 저질의 식별)

  • Lee, Dae-Jae
    • Korean Journal of Fisheries and Aquatic Sciences
    • /
    • v.51 no.3
    • /
    • pp.321-327
    • /
    • 2018
  • To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.

Seafloor Classification Based on the Texture Analysis of Sonar Images Using the Gabor Wavelet

  • Sun, Ning;Shim, Tae-Bo
    • The Journal of the Acoustical Society of Korea
    • /
    • v.27 no.3E
    • /
    • pp.77-83
    • /
    • 2008
  • In the process of the sonar image textures produced, the orientation and scale factors are very significant. However, most of the related methods ignore the directional information and scale invariance or just pay attention to one of them. To overcome this problem, we apply Gabor wavelet to extract the features of sonar images, which combine the advantages of both the Gabor filter and traditional wavelet function. The mother wavelet is designed with constrained parameters and the optimal parameters will be selected at each orientation, with the help of bandwidth parameters based on the Fisher criterion. The Gabor wavelet can have the properties of both multi-scale and multi-orientation. Based on our experiment, this method is more appropriate than traditional wavelet or single Gabor filter as it provides the better discrimination of the textures and improves the recognition rate effectively. Meanwhile, comparing with other fusion methods, it can reduce the complexity and improve the calculation efficiency.

An Acoustic Study of Prosodic Features of Korean Spoken Language and Korean Folk Song (Minyo) (언어와 민요의 운율 자질에 관한 음향음성학적 연구)

  • Koo, Hee-San
    • Speech Sciences
    • /
    • v.10 no.3
    • /
    • pp.133-144
    • /
    • 2003
  • The purpose of this acoustic experimental study was to investigate interrelation between prosodic features of Korean spoken language and those of Korean folk songs. The words of Changbutaryoung were spoken for analysis of spoken language by three female graduate students and the song was sung for musical features by three Kyunggi Minyo singers. Pitch contours were analyzed from sound spectrogram made by Pitch Works. Results showed that special musical voices (breaking, tinkling, vibrating, etc.) and tunes (rising, falling, level, etc) of folk song were discovered at the same place where accents of spoken language came. It appeared that, even though the patterns of pitch contour were different from each other, there was positive interrelation between prosodic features of Korean spoken language and those of Korean folk songs.

  • PDF

Evaluation of AE Signal caused by the Fatigue Crack (피로균열시 발생되는 AE신호 분석)

  • Kim, Jae-Gu;Gu, Dong-Sik;Choi, Byeong-Keun
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • 2011.04a
    • /
    • pp.572-577
    • /
    • 2011
  • The acoustic emission (AE) technique is a well-known non-destructive test technique, both in research and for industrial applications. It is mainly used to monitor the onset of cracking processes in materials and components. Predicting and preventing the crack phenomenon has attracted the attention of many researchers and has continued to provide a large incentive for the use of condition monitoring techniques to detect the earliest stages of cracks. In this research, goal is in grasping features of AE signal caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate feature of divided into three by crack length, the time waveform and the power spectrum were generated by the raw signals and the transferred signal processed by envelope analysis with DWT.

  • PDF