• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.55-62
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• 2017

Non-negative matrix factorization (NMF) is one of the typical parts-based representation in which images are expressed as a linear combination of basis vectors that show the lcoal features or objects in the images. In this paper, we represent face images using various NMF methods and recognize their face identities based on extracted features using a learning vector quantization. We analyzed the various NMF methods by comparing extracted basis vectors. Also we confirmed the availability of NMF to the face recognition by verification of recognition rate of the various NMF methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.95-102
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• 2012

Interactive drama is a story which requires user's free choice and participation. In this study, we grasp user's preference by making training data that utilize characters of interactive drama. Furthermore, we describe process of implementing systems which recommend new users path of stories that correspond with their preference. We used PCA and NMF to extract characteristic of preference. The success rate of recommending was 75% with PCA, while 62.5% with NMF.

• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.234-239
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• 2015

Non-Negative Matrix Factorization (NMF) is a method for updating dictionary and gain in alternating manner. Due to ease of implementation and intuitive interpretation, NMF is widely used to detect and separate overlapping sound events. However, NMF that utilizes non-negativity constraints generates parts-based representation and this distinct property leads to a dictionary containing fragmented acoustic events. As a result, the presence of shared basis results in performance degradation in both separation and detection tasks of overlapping sound events. In this paper, we propose a new method that utilizes K-Singular Value Decomposition (K-SVD) based dictionary to address and mitigate the part-based representation issue during the dictionary learning step. Subsequently, we calculate the gain using NMF in sound event detection step. We evaluate and confirm that overlapping sound event detection performance of the proposed method is better than the conventional method that utilizes NMF based dictionary.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.224-227
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• 2016

본 논문에서는 스테레오 채널 신호 간 강도비를 이용하여 음원을 분리하는 EADRess 알고리즘과 부분기반 표현을 특징으로 한 비음수 행렬 인수분해를 통해 음원을 분리하는 NMF 가 결합된 새로운 음원분리 알고리즘을 제안한다. 입력 오디오 신호로부터 frequency-azimuth 평면 구성을 통해 식별된 방위각에 상응하는 신호 강도비로 표현되는 확률밀도함수를 이용하여 1 단계 음원분리를 수행하고, 얻어진 개별 분리음원을 대상으로 supervised NMF 및 Wiener 필터 기반 마스킹 함수를 적용함으로써 잔류 혼합성분을 제거하는 2 단계 음원분리를 수행한다. 제안된 EADRess/NMF 결합 음원분리 알고리즘의 성능을 검증하기 위하여 SASSEC 에서 제공하는 테스트 음원을 이용하여 측정한 결과, 개별 음원분리 알고리즘에 비해 SIR 이 각각 1.41dB, 10.43dB 향상된 결과를 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.427-430
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• 2008

본 논문은 비음수 행렬 인수분해(NMF, non-negative matrix factorization)와 NMF 군집방법을 이용하여 다중문서를 요약하는 새로운 방법을 제안하였다. 본 논문에서 NMF에 의해 계산된 의미 특징(semantic feature)은 문서의 고유 구조(inherent structure)를 반영하여 문장을 추출함으로써 요약의 질을 높일 수 있고, 의미 변수(semantic variable)를 이용한 문장의 군집은 문장 간의 유사성과 다양성 고려하여서 쉽게 과잉정보를 제거하여 문장을 요약할 수 있는 장점을 갖는다.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1412-1417
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• 2004

Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.317-331
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• 2012

In this paper, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel non-negative matrix factorization (NMF) method. The NMF algorithm is a famous algorithm which is used to solve the source separation problems. In this paper, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010) for evaluation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.196-202
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• 2013

Basal cell carcinoma (BCC) is the most common skin cancer and its incidence is increasing rapidly. In this paper, we propose a diagnosis method of basal cell carcinoma by Raman spectra of skin tissue using the NMF(non-negative matrix factorization) algorithm. After preprocessing steps, measured Raman spectra is used classification experiments. The weight and the basis can be obtained in a simple matrix operation and a column vector of the matrix decompsed by the NMF. Linear combination of bases and weights, it is possible to approximate the average of Raman spectra. The classification method is to select the class which to minimize the root mean square of the difference of the linear combination and the objective spectrum. According to the experimental results, the proposed method shows the promising results to diagnosis BCC. In addition, it confirmed that the proposed method compared with the previous research result could be effectively applied in the analysis of the Raman spectra.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.197-204
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• 2013

In this paper, we propose a new method for artificial bandwidth extension of narrow-band signal in frequency domain. In the proposed method, a narrow-band signal is decomposed into excitation signal and spectral envelope, which are extended independently in frequency domain. The excitation signal is extended such that low-band harmonic structure is maintained in high band, and the spectral envelope is extended based on sub-band energy using NMF. Finally, the spectral phase is determined based on signal correlation between frames in time domain, resulting in the final wide-band signal. The subjective evaluation verified that the wide-band signal generated by the proposed method has a higher quality than the original narrow-band signal.