• 대한음성학회지:말소리
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• 제61호
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• 대한음성학회지:말소리
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• 제46호
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• pp.37-50
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• 2003

In this paper, we evaluate and compare the performance of speech/music discrimination based on various feature parameters and classifiers. As for feature parameters, we consider High Zero Crossing Rate Ratio (HZCRR), Low Short Time Energy Ratio (LSTER), Spectral Flux (SF), Line Spectral Pair (LSP) distance, entropy and dynamism. We also examine three classifiers: k Nearest Neighbor (k-NN), Gaussian Mixure Model (GMM), and Hidden Markov Model (HMM). According to our experiments, LSP distance and phoneme-recognizer-based feature set (entropy and dunamism) show good performance, while performance differences due to different classifiers are not significant. When all the six feature parameters are employed, average speech/music discrimination accuracy up to 96.6% is achieved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.7-11
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• 2010

In this paper, we propose an improved speech/music discrimination method based on a feature combination and dimensionality reduction approach. To improve discrimination ability, we use a feature based on spectral duration analysis and employ the hierarchical dimensionality reduction (HDR) method to reduce the effect of correlated features. Through various kinds of experiments on speech and music, it is shown that the proposed method showed high discrimination results when compared with conventional methods.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.149-152
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• 2003

In this paper, we present a performance comparison of feature parameters and classifiers for speech/music discrimination. Experiments were carried out on six feature parameters and three classifiers. It turns out that three classifiers shows similar performance. The feature set that captures the temporal and spectral structure of the signal yields good performance, while the phone-based feature set shows relatively inferior performance.

• Journal of information and communication convergence engineering
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• 제4권1호
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• pp.36-40
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• 2006

A new method of digital image analysis technique for medical images of cancer cell is presented. This paper deals with the cancer cell discrimination. The object images were the Thyroid Gland cell images that were diagnosed as normal and abnormal. This paper proposes a new discrimination method based on fuzzy logic algorithm. The focus of this paper is an automatic discrimination of cells into normal and abnormal of medical images by dominant feature parameters method with fuzzy algorithm. As a consequence of using fuzzy logic algorithm, the nucleus were successfully diagnosed as normal and abnormal. As for the experimental result, average recognition rate of 64.66% was obtained by applying single parameter of 16 feature parameters at a time. The discrimination rate of 93.08% was obtained by proposed method.

• 한국멀티미디어학회논문지
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• 제16권11호
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• pp.1281-1287
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• 2013

무분별한 복제 비디오를 막기 위하여 비디오 지문을 개발연구가 진행되고 있다. 이러한 비디오 지문들은 복제 비디오에서 발생되는 다양한 변화에 강인해야 하며 정확하게 구별될 수 있는 높은 분별력을 지녀야 한다. 일반적으로 비디오 지문들은 luminance(밝기), gradient(기울기), 그리고 DCT(주파수) 공간 등에서 주로 추출이 되고 있다. 그러나 아직 각 공간과 비디오 지문 사이에 실질적인 성능이 차이에 대한 연구가 부족하다. 따라서 본 논문에서는 각 공간에 따른 복제 비디오 검출 성능을 비교하기 위하여 강인함과 분별력에 기반한 복제 비디오 검출 실험을 진행하고 분석 하였다. 본 논문에서 동일한 패턴으로 각 공간에서 비디오 지문을 추출하고 각각 강인함과 분별력을 비교 한 후 최종적으로 복제 비디오 검출 실험을 진행하였다. 본 실험에서 DCT 공간에서 추출된 비디오 지문이 다른 공간보다 좀더 우수한 성능을 보여 주었는데 이는 해당 공간이 다른 비디오 지문들과 분별력이 가장 높았기 때문이다.

• 한국공작기계학회논문집
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• 제15권5호
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• pp.37-43
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• 2006

The target of this study is development of web based mold discrimination system by matching vision information with CAD database. The use of 2D vision image makes possible speedy mold discrimination from many databases. The image processing such as preprocessing, cleaning is done for obtaining vivid image with object information. The web-based system is a program which runs to exchange messages between a server and a client by making of ActiveX control and the result of mold discrimination is shown on web-browser. For effective feature classification and extraction, signature method is used to make sensible information from 2D data. As a result, the possibility of proposed system is shown as matching feature information from vision image with CAD database samples.

• 한국음향학회지
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• 제26권5호
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• pp.207-213
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• 2007

본 연구에서는 스펙트럼 분석과 신경망을 이용한 효과적인 음성/음악 분류 방법을 제안한다. 제안하는 방법은 스펙트럼을 분석하여 스펙트럴 피크 트랙에서 지속성 특징 파라미터인 MSDF(Maximum Spectral Duration Feature)를 추출하고 기존의 특징 파라미터인 MFSC(Mel Frequency Spectral Coefficients)와 결합하여 음성/음악 분류기의 특징으로 사용한다. 그리고 신경망을 음성/음악 분류기로 사용하였으며, 제안하는 방법의 성능 평가를 위해 학습 패턴 선별과 양, 신경망 구성에 따른 다양한 성능 평가를 수행하였다. 음성/음악 분류 결과 기존의 방법에 비해 성능 향상과 학습 패턴의 선별과 모델 구성에 따른 안정성을 확인할 수 있었다. MSDF와 MFSC를 특징 파라미터로 사용하고 50초 이상의 학습 패턴을 사용할 때 음성에 대해서는 94.97%, 음악에 대해서는 92.38%의 분류율을 얻었으며, MFSC만 사용할 때보다 음성은 1.25%, 음악은 1.69%의 향상된 성능을 얻었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.243-244
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• 2006

In this study, we propose a speech/music discrimination method using spectral peak track analysis. The proposed method uses the spectral peak track's duration at the same frequency channel for feature parameter. And use the duration threshold to discriminate the speech/music. Experiment result, correct discrimination ratio varies according to threshold, but achieved a performance comparable to another method and has a computational efficient for discrimination.

• 대한의용생체공학회:의공학회지
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• 제10권2호
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• pp.106-108
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• 1989

A new method of digital image analysis technique for discrimination of cancer cell was presented in this paper. The object image was the Thyroid eland cells image that was diagnosed as normal and abnormal (two types of abnormal: follicular neoplastic cell, and papillary neoplastic cell), respectively. By using the proposed region segmentation algorithm, the cells were segmented into nucleus. The 16 feature parameters were used to calculate the features of each nucleus. A9 a consequence of using dominant feature parameters method proposed in this paper, discrimination rate of 91.11% was obtained for Thyroid Gland cells.