In this paper, we present a method for the automatic transcription of polyphonic piano music. The input to this method consists in piano music recordings stored in WAV files, while the pitch of all the notes in the corresponding score forms the output. This method operates on a frame-by-frame basis and exploits a suitable time-frequency representation of the audio signal. The solution proposed ...

A central problem in the emerging field of metabolomics is how to identify the compounds comprising a chemical mixture of biological origin. NMR spectroscopy can greatly assist in this identification process, by means of multi-dimensional correlation spectroscopy, particularly total correlation spectroscopy (TOCSY). This Communication demonstrates how non-negative matrix factorization (NMF) pro...

Upon excitation with different wavelengths of light, biological tissues emit distinct but related autofluorescence signals. We used non-negative matrix factorization (NMF) to simultaneously decompose co-registered hyperspectral emission data from human retinal pigment epithelium/Bruch's membrane specimens illuminated with 436 and 480 nm light. NMF analysis was initialized with Gaussian mixture ...

Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation. Inspired by the expressive power of deep learning, several NMF variants equipped with deep architectures have been proposed. However, these methods mostly use the only nonnegativity while ignoring task-specific features of data. In this paper, we propose a novel deep approximately orthogonal nonnegative m...

In this paper, we present a new traffic-mining approach for automatic unveiling of typical global evolution of large-scale road networks. Our method uses as input a history of continuous traffic states (typically measured by travel times) of *all* links of the road graph. This historical data concatenated in a link/time matrix is then approximated with a locality-preserving Non-negative Matrix ...

We present in this paper an integrated service discovery framework based on Non-negative Matrix Factorization (NMF). NMF provides an effective means to cluster high-dimensional sparse data with both high clustering accuracy and good interpretability of the clustering result. This makes NMF especially suitable for service community discovery by clustering the Web service description data. Nevert...

Nonnegative Matrix Factorization (NMF) approximates a given data matrix as a product of two low rank nonnegative matrices, usually by minimizing the L2 or the KL distance between the data matrix and the matrix product. This factorization was shown to be useful for several important computer vision applications. We propose here a new NMF algorithm that minimizes the Earth Mover’s Distance (EMD) ...

Visual attributes are high-level semantic description of visual data that are close to the language of human. They have been intensively used in various applications such as image classification [1,2], active learning [3,4], and interactive search [5]. However, the usage of attributes in dimensionality reduction has not been considered yet. In this work, we propose to utilize relative attribute...

This document contains derivations for the method-of-moments algorithms used in the paper. The first section describes the general approach to deriving multiplicative update rules for the components of a non-negative matrix factorization (NMF). The next section discusses the convergence properties of NMF. Then we give the pseudocode of the sequence clustering algorithm for mixture of HMMs. And ...

We introduce non-negative matrix factorization with orthogonality constraints (NMFOC) for detection of a target spectrum in a given set of Raman spectra data. An orthogonality measure is defined and two different orthogonality constraints are imposed on the standard NMF to incorporate prior information into the estimation and hence to facilitate the subsequent detection procedure. Both multipli...