• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.363-364
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• 2006

The direction of arrival of the sound signal can be derived from the time differences at the microphone array and the motor controls the camera to point at the direction of the sound signal. You can get through to the homepage and confirm the camera image on a client computer which connects to the server computer through Internet.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.341-342
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• 2006

The direction of arrival of the sound signal can be derived from the time differences at the microphone array and the motor controls the camera to point at the direction of the sound signal. You can get through to the homepage and confirm the camera image on a client computer which connects to the server computer through Internet.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.53-59
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• 2020

This paper relates to an IoT device system that detects sound source and estimates the sound source location. More specifically, it is a system using a sound source direction detection device that can accurately detect the direction of a sound source by analyzing the difference of arrival time of a sound source signal collected from microphone sensors, and track the generation direction of a sound source using an IoT sensor. As a result of a performance test by generating a sound source, it was confirmed that it operates very accurately within 140dB of the acoustic detection area, within 1 second of response time, and within 1° of directional angle resolution. In the future, based on this design plan, we plan to commercialize it by improving the reliability by reflecting the artificial intelligence algorithm through big data analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.71-77
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• 2005

In this paper we implement a headphone-based 5.1 channel 3-dimensional (3D) sound generation system using HRTF (Head Related Transfer Function). Each mono sound source in the 5.1 channel signal is localized on its virtual location by binaural filtering with corresponding HRTFs, and reverberation effect is added for spatialization. To reduce the computational burden, we reduce the number of taps in the HRTF impulse response and model the early reverberation effect with several tens of impulses extracted from the whole impulse sequences. We modified the spectrum of HRTF by weighing the difference of front-back spec01m to reduce the front-back confusion caused by non-individualized HRTF DB. In informal listening test we can confirm that the implemented 3D sound system generates live and rich 3D sound compared with simple stereo or 2 channel down mixing.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.837-848
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• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.233-244
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• 2021

Recently, several ICT-based cat studies have produced some successful results, according to academic and industry sources. However, research on the level of simply identifying the cat's condition, such as the behavior and sound classification of cats based on images and sound signals, has yet to be found. In this paper, based on the veterinary scientific knowledge of cats, a practical and academic cat monitoring and disease diagnosis system is proposed to monitor the health status of the cat 24 hours a day by automatically categorizing and analyzing the behavior of the cat with location information using LSTM with a beacon sensor and a raspberry pie that can be built at low cost. Validity of the proposed system is verified through experimentation with cats in actual custody (the accuracy of the cat behavior classification and location identification was 96.3% and 92.7% on average, respectively). Furthermore, a rule-based disease analysis system based on the veterinary knowledge was designed and implemented so that owners can check whether or not the cats have diseases at home (or can be used as an auxiliary tool for diagnosis by a pet veterinarian).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.71-78
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• 2009

Sound source localization systems in service robot applications estimate the direction of a human voice. Time delay information obtained from a few separate microphones is widely used for the estimation of the sound direction. Correlation is computed in order to calculate the time delay between two signals. In addition, PHAT weighting function can be applied to significantly improve the accuracy of the estimation. However, FFT and IFFT operations in the PHAT weighting function occupy more than half of the area of the sound source localization system. Thus efficient FFT and IFFT designs are essential for the IP implementation of sound source localization system. In this paper, we propose an efficient FFT/IFFT design method based on the characteristics of human voice.

• 한국ITS학회:학술대회논문집
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• 2005.11a
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• pp.62-66
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• 2005