• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.46-53
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• 2001

The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.407-412
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• 2019

In this paper, we propose a technique for processing of speech recognition in korean words. Speech recognition is a technology that converts acoustic signals from sensors such as microphones into words or sentences. Most foreign languages have less difficulty in speech recognition. On the other hand, korean consists of vowels and bottom consonants, so it is inappropriate to use the letters obtained from the voice synthesis system. That improving the conventional structure speech recognition can the correct words recognition. In order to solve this problem, a new algorithm was added to the existing speech recognition structure to increase the speech recognition rate. Perform the preprocessing process of the word and then token the results. After combining the result processed in the Levenshtein distance algorithm and the hashing algorithm, the normalized words is output through the consonant comparison algorithm. The final result word is compared with the standardized table and output if it exists, registered in the table dose not exists. The experimental environment was developed by using a smartphone application. The proposed structure shows that the recognition rate is improved by 2% in standard language and 7% in dialect.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.580-586
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• 2019

In this paper, we propose a biomimetic communication method using a dolphin whistle to covertly transmit the communication signal. A conventional CSS (Chirp Spread Spectrum) modulation technique divides dolphin whistle into several slots and modulates with up and down chirp signals. That causes the time-frequency characteristic difference between the original dolphin whistle and the camouflage performance is degraded. In this paper, we propose a delay based modulation scheme to eliminate distortions. The simulation results show that the bit error rate of the proposed method is better performance than that of the conventional CSS modulation method by about 3.5 dB to 8 dB. And the camouflage performance that evaluated through the cross correlation in the time-frequency domain is also better than that of the CSS modulation method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.370-381
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• 2021

This paper presented an efficient transceiver structure of multiband Frequency Shift Keying (FSK) signals with direct sequence spread spectrum for maintaining covertness and performance. In aspect to covertness, direct sequence spread spectrum method, which multiplying by Pseudo Noise (PN) codes whose rate is much higher than that of data sequence, is employed. In aspect to performance, in order to overcome performance degradation caused by multipath and Doppler spreading, we applied multiband, turbo equalization, and weighting algorithm are applied. Based on the simulation results, by applying 4 number of multiband and number of chips are 8 and 32, experiments were conducted in a lake with a distance of moving from 300 m to 500 m between the transceivers. we confirmed that the performance was improved as the number of bands and chips are increased. Furthermore, the performance of multiband was improved when the proposed weighting algorithm was applied.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.154-159
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• 2019

When measuring the radiating noise of an underwater moving source, the range information between the acoustic source and the receiver is an important evaluation factor, and the measurement standards such as a receiver position, a moving source depth and a speed are set. Although there is a method of using the cross correlation as a method of finding the range of the underwater moving source, this method requires a time synchronization process. In this paper, we proposed the method to estimate the range by comparing the Doppler frequency difference of the theoretically calculated multipath signal with the Doppler frequency difference of the multipath signal estimated from the received signal. The proposed method does not require a separate time synchronization process. Simulations were performed to verify the performance, and the ranging error of the proposed method reduced by about 95 % than that of the conventional method.

• Journal of Platform Technology
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• v.9 no.3
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• pp.44-51
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• 2021

In general, golden time refers to the most important time in the initial response to accidents such as saving lives or extinguishing fires. The golden time varies from disaster to disaster, but is aimed at five minutes in terms of fire and first aid. However, for the actual site, the average dispatch time for ambulances is 9 minutes and the average transfer time is 17.6 minutes, which is quite large compared to the golden time. There are various causes for this delay, but the main cause is traffic jams. In order to solve the problem, the government has established emergency car concession obligations and secured golden time to prioritize ambulances in places with the highest accident rate, but it is not a solution in rush hour when traffic is increasing rapidly. Therefore, this paper proposed a deep learning-based emergency vehicle priority signal system using collected sound data by installing sound sensors on traffic lights and conducted an experiment to classify frequency signals that differ depending on the distance of the emergency vehicle.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.219-227
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• 2021

This paper proposed an Orthogonal Frequency Division Multiplexing (OFDM) based biomimetic communication method using a dolphin whistle which covertly transmits communication signals to allies. The proposed method divides the dolphin whistle into several time slots corresponding to a number of OFDM symbols, and modulates the communication signal by mapping differential phase shift keying (DPSK) symbols into subcarriers that have the frequency bands of the dolphin whistle in each slot. The advantages of the proposed method are as follows: In the conventional Chirp Spread Spectrum (CSS) and Frequency Shift Keying (FSK) based biomimetic communication methods, the discontinuity of the frequency contour is large, but the proposed method can reduce the discontinuity. Even if the modulation order is increased, the degradation of the mimicking performance is small. The computer simulations demonstrate that the Bit Error Rate (BER) and mimicking performance of the proposed method are better performance than those of the conventional CSS and FSK.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.