• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.102-110
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• 2010

Music transcription is extracting pitch (the height of a musical note) and rhythm (the length of a musical note) information from audio file and making a music score. In this paper, we decomposed a waveform into frequency and rhythm components using Non-Negative Matrix Factorization (NMF) and Non-Negative Sparse coding (NNSC) which are often used for source separation and data clustering. And using the subharmonic summation method, fundamental frequency is calculated from the decomposed frequency components. Therefore, the accurate pitch of each score can be estimated. The proposed method successfully performed music transcription with its results superior to those of the conventional methods which used either NMF or NNSC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.418-420
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• 2013

본 논문은 클라우드 기반의 연관피드백과 비음수행렬분해의 의미특징에 의한 용어 가중치 재 산정에 의한 문서요약 방법을 제안한다. 제안된 방법은 연관피드백을 이용하여 사용자의 의도를 문서요약 결과에 반연하며, 클라우드 기반의 비음수행렬분해의 의미특징으로 용어의 가중치를 재 산정함으로서 문장집합의 내부 특징을 잘 나타나기 때문에 문서요약의 질을 향상할 수 있다. 또한 클라우드 기반으로 대량의 빅데이터로부터 효율적으로 문서를 요약할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.11-18
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• 2008

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user's requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user's requirement for document classification and the document clusters by means of machine. The Proposed method can improve the quality of document clustering because the re-weighted terms. the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.39-48
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• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.23-29
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• 2011

There have been many studies on red tide due to increasing damage from red tide on fishing and aquaculture industry. However, internal study of automatic red tide image classification is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies used a few type of red tide algae for image classification. In this paper, we proposed the red tide image recognition method using semantic features of NMF and roundness of image objects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.418-420
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• 2015

본 논문은 베이지안 비음수 행렬 인수분해 (Bayesian nonnegative matrix factorization, BNMF) 기반의 음성 강화 기법에서 음성과 잡음 성분의 latent source 수에 따른 강화성능에 대해 서술한다. BNMF 기반의 음성 강화 기법은 입력 신호를 서브 신호들의 합으로 분해한 후, 잡음 성분을 제거하는 방식으로 그 성능이 기존의 NMF 기반의 방법들보다 우수한 것으로 알려져 있다. 그러나 많은 계산량과 latent source 의 수에 따라 성능의 차이가 있다는 단점이 있다. 이러한 단점을 개선하기 위해 본 논문에서는 BNMF 기반의 음성 강화 기법에서 최적의 latent source 개수를 찾기 위한 실험을 진행하였다. 실험은 잡음의 종류, 음성의 종류, 음성과 잡음의 latent source 의 개수, 그리고 SNR 을 바꿔가며 진행하였고, 성능 평가 방법으로 PESQ (perceptual evaluation of speech quality) 를 이용하였다. 실험 결과, 음성의 latent source 개수는 성능에 영향을 주지 않지만, 잡음의 latent source 개수는 많을수록 성능이 좋은 것으로 확인되었다.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.502-508
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• 2008

With the increased use of the internet and the tremendous amount of data it transfers, it is more necessary to summarize documents. We propose a new method using the Non-negative Semantic Variable Matrix (NSVM) and the generic important of semantic features obtained by Non-negative Matrix Factorization (NMF) to extract the sentences for automatic generic summarization. The proposed method use non-negative constraints which is more similar to the human's cognition process. As a result, the proposed method selects more meaningful sentences for summarization than the unsupervised method used the Latent Semantic Analysis (LSA) or clustering methods. The experimental results show that the proposed method archives better performance than other methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.53-60
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• 2012

In this paper, we proposes a new document summarization method using the expanded query by wikipedia and the semantic feature representing inherent structure of document set. The proposed method can expand the query from user's initial query using the relevance feedback based on wikipedia in order to reflect the user require. It can well represent the inherent structure of documents using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can reduce the semantic gap between the user require and the result of document summarization to extract the meaningful sentences using the expanded query and semantic features. The experimental results demonstrate that the proposed method achieves better performance than the other methods to summary document.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.851-858
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• 2016

This paper presents a new statistical voice activity detection (VAD) based on the probabilistic interpretation of nonnegative matrix factorization (NMF). The objective function of the NMF using Kullback-Leibler divergence coincides with the negative log likelihood function of the data if the distribution of the data given the basis and encoding matrices is modeled as Poisson distributions. Based on this probabilistic NMF, the VAD is constructed using the likelihood ratio test assuming that speech and noise follow Poisson distributions. Experimental results show that the proposed approach outperformed the conventional Gaussian model-based and NMF-based methods at 0-15 dB signal-to-noise ratio simulation conditions.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.121-128
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• 2017

In this paper, we propose a dual-channel acoustic event detection (AED) method using nonnegative tensor factorization (NTF) and hidden Markov model (HMM) in order to improve detection accuracy of AED in multisource environments. The proposed method first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique applied to dual-channel input signals. After that, an HMM-based likelihood ratio test is carried out to verify the detected events by using channel gains. The detection accuracy of the proposed method is measured by F-measures under 9 different multisource conditions. Then, it is also compared with those of conventional AED methods such as Gaussian mixture model and nonnegative matrix factorization. It is shown from the experiments that the proposed method outperforms the convectional methods under all the multisource conditions.