• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.522-525
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• 2009

This paper proposed the content-based method of classifying filthy Phornographic video, which causes a big problem of modern society as the reverse function of internet. Audio data was used to extract the features from Phornographic video. There are frequency spectrum, autocorrelation, and MFCC as the feature of audio used in this paper. The sound that could be filthy contents was extracted, and the Phornographic was classified by measuring how much percentage of relevant sound was corresponding with the whole audio of video. For the experiment on the proposed method, The efficiency of classifying Phornographic was measured on each feature, and the measured result and comparison with using multi features were performed. I can obtain the better result than when only one feature of audio was extracted, and used.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.73-83
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• 2014

Thanks to the development of diverse audio editing Technology, audio file can be easily revised. As a result, diverse social problems like forgery may be caused. Digital forensic technology is actively studied to solve these problems. In this paper, a hand-held device identification method, an area of digital forensic technology is proposed. It uses the noise features of devices caused by the design and the integrated circuit of each device but cannot be identified by the audience. Wiener filter is used to get the noise sounds of devices and their acoustic features are extracted via MIRtoolbox and then they are trained by multi-layer neural network. To evaluate the proposed method, we use 5-fold cross-validation for the recorded data collected from 6 mobile devices. The experiments show the performance 99.9%. We also perform some experiments to observe the noise features of mobile devices are still useful after the data are uploaded to UCC. The experiments show the performance of 99.8% for UCC data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.7
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• pp.72-79
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• 2021

This study reports on the experiment of using deep learning algorithms to determine the machining process of aluminium and steel. A face cutting milling tool was used for machining and the cutting speed was set between 3 and 4 mm/s. Both materials were machined with a depth to 0.5mm and 1.0mm. To demonstrate the developed deep learning algorithm, simulation experiments were performed using the VGGish algorithm in MATLAB toobox. Downcutting was used to cut aluminum and steel as a machining process for high quality and precise learning. As a result of learning algorithms using audio data, 61%-99% accuracy was obtained in four categories: Al 0.5mm, Al 1.0mm, Steel 0.5mm and Steel 1.0mm. Audio discrimination using deep learning is derived as a probabilistic result.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.349-350
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• 2012

본 논문에서는 다자간 영상통화의 오디오 게인콘트롤을 위한 저연산 음성분류방식을 제안한다. 제안된 음성분류방식은 입력되는 음성신호를 음성신호의 특징에 따라서 묵음/무성음/유성음으로 분류한다. 입력된 음성신호의 에너지를 이용해서 음성구간과 비음성구간을 판별한다. 음성구간으로 판별된 구간에 대해서 ZCR(Zeor Crossing Rate)를 이용하여 유성음과 무성음으로 분류한다. 제안된 방식의 성능을 측정을 위해 음성분류 정확도와 연산시간을 측정하여 성능을 측정하였다.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.151-156
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• 2009

This paper proposes a Gaussian mixture model(GMM)-based music discrimination system for FM broadcasting. The objective of the system is automatically archiving music signals from audio broadcasting programs that are normally mixed with human voices, music songs, commercial musics, and other sounds. To improve the system performance, make it more robust and to accurately cut the starting/ending-point of the recording, we also added a post-processing module. Experimental results on various input signals of FM radio programs under PC environments show excellent performance of the proposed system. The fixed-point simulation shows the same results under 3MIPS computational power.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.25-28
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• 2007

본 논문은 FM 라디오 방송의 오디오 신호를 블록 단위로 음성 음악을 판별하는 시스템을 제안하는 논문이다. 본 논문에서는 음성 음악 판별 시스템을 구축하기 위해 다양한 특정 파라미터와 분류 알고리즘을 제안 한다. 특정 파라미터는 신호처리 분야(Centroid, Rolloff, Flux, ZCR, Low Energy), 음성 인식 분야(LPC, MFCC), 음악 분석 분야(MPitch, Beat)에서 각각 사용되는 파라미터를 사용하였으며 분류 알고리즘으로는 패턴인식 분야(GMM, KNN, BP)와 퍼지 신경망(ANFIS)을 사용하였고, 거리 구현은 Mahalanobis 거리를 사용하였다.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.57-62
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• 1997

Two psychoacoustic models used in MPEG-1 employ different masking patterns, different masking indexes, and different computational procedures. As a result, Model 1 is inferior to Model 2 due to its worst case approach in computing the SMR even though it determines tonality and masking levels accurately. In this study, we investigate the performances of psychoacoustic models when we modify the MPEG-1 audio coder for variable bitrates. Simulation results show that Model 2 has a gain of 30 kbps in the dual channel mode and 20 kbps in the joint stereo mode. It is generally known that the joint stereo mode has a gain in bitrate compare to the dual channel mode. For signals with frequent attacks, this gain becomes larger in Model 1 than in Model 2. This is due to the fact that Model 1 uses the worst case approach in computing the SMR to reduce pre-echo

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.246-249
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• 2005

음향 시스템에 있어서 하울링 현상은 음성 레벨을 제한함으로써 음성의 명료도를 저하시키는 주된 요인이다. 그리고 이를 해결하기 위한 방법으로 하울링 주파수 대역의 게인을 낮추어 음향신호의 피드백을 최소화 하는 것이 일반적이기 때문에 하울링 주파수를 찾아내는 것이 하울링 제어에 있어서 가장 핵심적인 요소가 된다. 그래서 본 논문에서는 하울링 주파수를 자동으로 검출할 수 있는 기법을 제시하였다. 이는 외부로부터 입력된 오디오신호가 하울링 신호 특성을 만족하는 정도를 ‘하울링 지수’라는 파라메터로 정의한 후 이를 기준으로 하울링 발생여부를 판단하고 하울링으로 판별된 신호의 최대 진폭을 갖는 주파수를 하울링 주파수로 출력하는 기법이다. 본 하울링 신호 자동 검출기법의 내용을 검증하기 위하여 하울링 자동 검출 프로그램을 제작하여 실험을 수행한 결과 전체 하울링 신호의 95% 이상을 검출할 수 있었다.

• MALSORI
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• no.63
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• pp.139-151
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• 2007

As the Internet becomes prevalent in our lives, harmful contents, such as phornographic videos, have been increasing on the Internet, which has become a very serious problem. To prevent such an event, there are many filtering systems mainly based on the keyword-or image-based methods. The main purpose of this paper is to devise a system that classifies pornographic videos based on the audio information. We use the mel-cepstrum modulation energy (MCME) which is a modulation energy calculated on the time trajectory of the mel-frequency cepstral coefficients (MFCC) as well as the MFCC as the feature vector. For the classifier, we use the well-known Gaussian mixture model (GMM). The experimental results showed that the proposed system effectively classified 98.3% of pornographic data and 99.8% of non-pornographic data. We expect the proposed method can be applied to the more accurate classification system which uses both video and audio information.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.81-88
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• 2010

In this paper, we proposed an automatic music/non-music signal discrimination system from broadcasting audio signal as a preliminary study of building a sound source monitoring system in real broadcasting environment. By reflecting human speech articulation characteristics, we used three simple time-domain features such as energy standard deviation, log energy standard deviation and log energy mean. Based on the experimental threshold values of each feature, we developed a rule-based algorithm to classify music portion of the input audio signal. For the verification of the proposed algorithm, actual FM broadcasting signal was recorded for 24 hours and used as source input audio signal. From the experimental results, the proposed system can effectively recognize music section with the accuracy of 96% and non-music section with that of 87%, where the performance is good enough to be used as a pre-process module for the a sound source monitoring system.