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Musical instrument classification using nonnegative matrix factorization algorithms
 in Proc. International Symposium on Circuits and Systems (ISCAS 2006), Island of
, 2006
"... In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in sound classification applications as well as MPEG7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrum ..."
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Cited by 11 (0 self)
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instrument classes. Subsets of the feature set are selected using branchandbound search, obtaining the most suitable features for classification. A class of classifiers is developed based on the nonnegative matrix factorization (NMF). The standard NMF method is examined as well as its modifications
A novel discriminant nonnegative matrix factorization algorithm with applications to facial image characterization problems
 IEEE Transactions on Information Forensics and Security
"... Abstract—The methods introduced so far regarding discriminant nonnegative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. In order to remedy this limitation, a novel DNMF method is presented that uses projected gradients. The proposed algorithm employs some ex ..."
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Cited by 18 (5 self)
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Abstract—The methods introduced so far regarding discriminant nonnegative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. In order to remedy this limitation, a novel DNMF method is presented that uses projected gradients. The proposed algorithm employs some
FastNMF: A Fast Monotonic Fixedpoint Nonnegative Matrix Factorization Algorithm with High Ease of Use 1
"... Nonnegative Matrix Factorization (NMF) is a recently developed method for dimensionality reduction, feature extraction and data mining, etc. Currently no NMF algorithm holds both satisfactory efficiency for applications and enough ease of use. To improve the applicability of NMF, this paper propose ..."
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Cited by 1 (1 self)
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Nonnegative Matrix Factorization (NMF) is a recently developed method for dimensionality reduction, feature extraction and data mining, etc. Currently no NMF algorithm holds both satisfactory efficiency for applications and enough ease of use. To improve the applicability of NMF, this paper
A Novel Discriminant NonNegative Matrix Factorization and its Application to Facial Expression Recognition
"... Abstract. The paper proposes a novel discriminant nonnegative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional nonnegative matrix factorization algorithms, the algorithm adds discriminant constraints in lowdimensional weights. The experiments on f ..."
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Abstract. The paper proposes a novel discriminant nonnegative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional nonnegative matrix factorization algorithms, the algorithm adds discriminant constraints in lowdimensional weights. The experiments
BMC Bioinformatics BioMed Central Methodology article
, 2006
"... LSNMF: A modified nonnegative matrix factorization algorithm utilizing uncertainty estimates ..."
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LSNMF: A modified nonnegative matrix factorization algorithm utilizing uncertainty estimates
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
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Cited by 1230 (5 self)
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Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
Nonnegative matrix factorization with sparseness constraints
 Jour. of
, 2004
"... www.cs.helsinki.fi/patrik.hoyer ..."
Singlechannel speech separation using sparse nonnegative matrix factorization
 in International Conference on Spoken Language Processing (INTERSPEECH
, 2006
"... We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording. The method of choice is a sparse nonnegative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied to ..."
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Cited by 61 (4 self)
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We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording. The method of choice is a sparse nonnegative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied
Extraction of Multiple Sound Sources from Monophonic Inputs
, 2004
"... In this paper we present an extension to the NonNegative Matrix Factorization algorithm which is capable of identifying components with temporal structure. We demonstrate the use of this algorithm in the magnitude spectrum domain, where we employ it to perform extraction of multiple sound objects f ..."
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In this paper we present an extension to the NonNegative Matrix Factorization algorithm which is capable of identifying components with temporal structure. We demonstrate the use of this algorithm in the magnitude spectrum domain, where we employ it to perform extraction of multiple sound objects
Monaural Sound Source Separation by Perceptually Weighted NonNegative Matrix Factorization
"... Abstract — A dataadaptive algorithm for the separation of sound sources from onechannel signals is presented. The algorithm applies weighted nonnegative matrix factorization on the power spectrogram of the input signal. Perceptually motivated weights for each critical band in each frame are used ..."
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Cited by 9 (0 self)
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Abstract — A dataadaptive algorithm for the separation of sound sources from onechannel signals is presented. The algorithm applies weighted nonnegative matrix factorization on the power spectrogram of the input signal. Perceptually motivated weights for each critical band in each frame are used
Results 1  10
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1,186,664