• The Journal of the Acoustical Society of Korea
• /
• v.41 no.6
• /
• pp.691-697
• /
• 2022

In this work, a straightforward component design of a direct current (DC) relay equipped in electric vehicles is discussed. The work aims to provide and evaluate effective measures for reducing high-frequency sound from the DC relay carrying electric power. From the operation experiments for the relay, it is observed that noise is caused by the resonance from the forced vibration by the electromagnetic repulsive force originating at the area of electric contacts with a resonance frequency of around 710 Hz ~ 730 Hz. A finite element model for the relay was established to conduct vibration mode analysis, consisting of stationary and movable contacts and a contact spring. Vibration mode analysis indicates that in the resonance frequency, the movable contact with two-point contacts experiences rotational vibration mode. For the proposed relay with a three-point contact, vibration mode analyses give reasonable results of reducing noise at that frequency. Furthermore, for the fabricated relays with the three-point contact, similar results have been obtained. In conclusion, one can see that the proposed measures provide one of the feasible solutions to the reduction of relay noise.

• Phonetics and Speech Sciences
• /
• v.15 no.2
• /
• pp.43-51
• /
• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.38.2-38.2
• /
• 2020

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1 kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. Inside the simulation box. we zoom-in on a high-resolution cuboid region with a volume of 1049 × 114 × 114 Mpc3. The subgrid physics chosen to model galaxy formation includes radiative heating/cooling, reionization, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OpenMP version of the RAMSES code, specifically targeted for modern many-core many thread parallel architectures. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder to analyse the large outputs of HR5. The simulation successfully reproduces many observations, such as the cosmic star formation history, connectivity of galaxy distribution and stellar mass functions. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1103-1108
• /
• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.7
• /
• pp.448-457
• /
• 2010

Vibrations and wave propagations in waveguide structures can be analysed efficiently by using waveguide finite element (WFE) method. The WFE method only models the 2-dimensional cross-section of the waveguide with finite elements so that the size of the model and computing time are much less than those of the 3-dimensional FE models. For cylindrical shells or pipes which have simple cross-sections, the external coupling with fluids can be treated theoretically. For waveguides of complex cross-sectional geometries, however, numerical methods are required to deal with external fluids. In this numerical approach, the external fluid is modelled by the boundary elements (BEs) and connected to WFEs. In order to validate this WFE/BE method, a pipe submerged in water is considered in this study. The dispersion diagrams and point mobilities of the pipe simulated are compared to those that theoretically obtained. Also the acoustic powers radiated from the pipe are predicted and compared in both cases of air and water as an external medium.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.5
• /
• pp.447-453
• /
• 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.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.162-167
• /
• 2024

Low frequency noise emitted by wind turbines is one of the most noise complaints. In this study, the reliability of the models was examined by comparing the measured sound pressure levels with the predicted levels based on Denish model and commercial programs of the SounPLAN and the ENPro based on ISO 9613. As a result of applying it to representative 3 MW wind turbines, on lnad, the measured and the predicted values differed within a maximum of 5 dB in the frequency range of 12.5 Hz to 80 Hz. It may be due to the change in the acoustic power levels because the wind turbines have been in operation for more than 7 years. However, considering that the Boundary Element Method (BEM) predicted value, which is known to be the most accurate in the low frequency band, the predicted values are well matched within 2.5 dB, the models of this study are expected to be used as deviation within 3 dB.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.1
• /
• pp.138-144
• /
• 2024

This paper presents a bistatic to monostatic conversion technique to analyze the bistatic target strength of submarines. The technique involves determining the transmission path length of acoustic waves, which are emitted from a source, scattered off an underwater target, and eventually received by a receiver. By generating a corresponding virtual scattering surface, this method effectively transforms the target strength analysis problem from bistatic to monostatic. The converted monostatic target strength problem can be assessed using a well-established monostatic numerical methods. The bistatic target strength analysis for Benchmark Target Strength Simulation (BeTTSi), a widely used target strength model were performed. The results were compared with those calculated by boundary element methods and Kirchhoff approximation, and confirmed the validity and the practical applicability of the proposed analysis technique for evaluating submarine target strength.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.4
• /
• pp.30-39
• /
• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.2
• /
• pp.150-159
• /
• 2002

The possibility of a broadband noise reduction of piezoelectric smart panels is experimentally studied. Piezoelectric smart panel is basically a plate structure on which piezoelectric patch with shunt circuits is mounted and sound absorbing material is bonded on the surface of the structure. Sound absorbing materials can absorb the sound transmitted at mid frequency region effectively while the use of piezoelectric shunt damping can reduce the transmission at resonance frequencies of the panel structure. To be able to tune the piezoelectric shunt circuit, the measured electrical impedance model is adopted. Resonant shunt circuit composed of register and inductor in stories is considered and the circuit parameters are determined based on maximizing the dissipated energy through the circuit. The transmitted noise reduction performance of smart panels is investigated using an acoustic tunnel. The tunnel is a square crosses sectional tunnel and a loud speaker is mounted at one side of the tunnel as a sound source. Panels are mounted in the middle of the tunnel and the transmitted sound pressure across the panels is measured. Noise reduction performance of a double smart panel possessing absorbing material and air gap shows a good result at mid frequency region except the first resonance frequency. By enabling the piezoelectric shunt damping, noise reduction is achieved at the resonance frequency as well. Piezoelectric smart panels incorporating passive method and piezoelectric shunt damping are a promising technology for noise reduction in a broadband frequency.