Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
1,186,664
Musical instrument classification using non-negative 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 MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrum ..."
Abstract
-
Cited by 11 (0 self)
- Add to MetaCart
instrument classes. Subsets of the feature set are selected using branchand-bound search, obtaining the most suitable features for classification. A class of classifiers is developed based on the non-negative matrix factorization (NMF). The standard NMF method is examined as well as its modifications
A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems
- IEEE Transactions on Information Forensics and Security
"... Abstract—The methods introduced so far regarding discrimi-nant non-negative 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 ..."
Abstract
-
Cited by 18 (5 self)
- Add to MetaCart
Abstract—The methods introduced so far regarding discrimi-nant non-negative 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 Fixed-point Non-negative Matrix Factorization Algorithm with High Ease of Use 1
"... Non-negative 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 ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Non-negative 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 Non-Negative Matrix Factorization and its Application to Facial Expression Recognition
"... Abstract. The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments on f ..."
Abstract
- Add to MetaCart
Abstract. The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments
BMC Bioinformatics BioMed Central Methodology article
, 2006
"... LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates ..."
Abstract
- Add to MetaCart
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Algorithms for Non-negative Matrix Factorization
- In NIPS
, 2001
"... Non-negative 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 ..."
Abstract
-
Cited by 1230 (5 self)
- Add to MetaCart
Non-negative 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
Non-negative matrix factorization with sparseness constraints
- Jour. of
, 2004
"... www.cs.helsinki.fi/patrik.hoyer ..."
Single-channel speech separation using sparse non-negative 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 non-negative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied to ..."
Abstract
-
Cited by 61 (4 self)
- Add to MetaCart
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 non-negative 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 Non-Negative 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 ..."
Abstract
- Add to MetaCart
In this paper we present an extension to the Non-Negative 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 Non-Negative Matrix Factorization
"... Abstract — A data-adaptive algorithm for the separation of sound sources from one-channel signals is presented. The algorithm applies weighted non-negative matrix factorization on the power spectrogram of the input signal. Perceptually motivated weights for each critical band in each frame are used ..."
Abstract
-
Cited by 9 (0 self)
- Add to MetaCart
Abstract — A data-adaptive algorithm for the separation of sound sources from one-channel signals is presented. The algorithm applies weighted non-negative 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
of
1,186,664
