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Probabilistic nonnegative matrix factorization (NMF) models have had great success in audio source separation problems. Bayesian formulations of these models are fit either using Markov chain Monte Carlo or variational inference, with the latter often being preferred for its computational efficiency. However, this computational efficiency comes at a cost; mean-field variational methods cannot represent...
In this paper, we present a framework for complex scenarios recognition with crowded walkers. This study aims to develop an alternative surveillance system to traditional video camera and visual sensor based systems. Instead of utilizing visual devices in traditional surveillance systems, our crowded scene analysis is based on PIR (Pyroelectric Infrared) sensor networks with intelligent algorithms...
In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent...
The fast growth of Internet web documents has posed new challenges on how to efficiently and accurately manage and retrieve the textual collections, text clustering plays a significant role. Traditional document clustering is an unsupervised categorization of a given document collection based on vector space model, which is a high sparse vector. In this paper, we propose a means to fight the existing...
Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Researchers have reported large number of lncRNA associated human diseases over the past decade. In this article we employed the Non-negative Matrix Factorization method to develop a low-dimensional computational model that can describe the existing knowledge about lncRNA-disease...
Hyperspectral unmixing is a hot topic in signal and image processing. A high-dimensional data can be decomposed into two non-negative low-dimensional matrices by Non-negative matrix factorization(NMF). However, the algorithm has many local solutions because of the non-convexity of the objective function. Some algorithms solve this problem by adding auxiliary constraints, such as sparse. The sparse...
Monophonic sound source separation is an essential subject on the fields where sound, such as voice, music and noise, is dealt with. In particular, unsupervised approaches to this problem have high versatility in comparison with supervised approaches. Non-negative matrix factorization is the most frequently used algorithm for the monophonic sound source separation without prior knowledge. This is...
Blind Source Separation (BSS) of underdetermined mixture has acquired a huge attention in signal processing environment, even though it is very much difficult to separate the underlying sources. The difficulty in source separation arise due to the mixing of large number of source signals in time and frequency, and propagation of it to one or more sensors through air. The objective in BSS is to identify...
This paper proposes a method to separate polyphonic music signal into signals of each musical instrument by NMF: Non-negative Matrix Factorization based on preservation of spectrum envelope. Sound source separation is taken as a fundamental issue in music signal processing and NMF is becoming common to solve it because of its versatility and compatibility with music signal processing. Our method bases...
We consider the problem of extracting descriptors that represent visually salient portions of a video sequence. Most state-of-the-art schemes generate video descriptors by extracting features, e.g., SIFT or SURF or other keypoint-based features, from individual video frames. This approach is wasteful in scenarios that impose constraints on storage, communication overhead and on the allowable computational...
Image collection visualization is an important component of exploration-based image retrieval systems. In this paper we address the problem of generating an image collection visualization in which images and text can be projected together. Given a collection of images with attached text annotations, our aim is to find a common representation for both information sources to model latent correlations...
In command-and-control applications, a vocal user interface (VUI) is useful for handsfree control of various devices, especially for people with a physical disability. The spoken utterances are usually restricted to a predefined list of phrases or to a restricted grammar, and the acoustic models work well for normal speech. While some state-of-the-art methods allow for user adaptation of the predefined...
Rare endmembers estimation is a very interesting and difficult issue in the unmixing field. We focus on the case of a rare endmember which appear as a change between two images of a same scene. We use both the information of the image where the new endmember is missing and change detection results to estimate the appearing endmember. We base the proposed approach on spectral unmixing with non negative...
In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel signal separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only...
Traditionally, NMF algorithms consist of two separate stages: a training stage, in which a generative model is learned; and a testing stage in which the pre-learned model is used in a high level task such as enhancement, separation, or classification. As an alternative, we propose a task-supervised NMF method for the adaptation of the basis spectra learned in the first stage to enhance the performance...
In this paper, we propose a new approach for addressing music signal separation based on the generalized Bayesian estimator with automatic prior adaptation. This method consists of three parts, namely, the generalized MMSE-STSA estimator with a flexible target signal prior, the NMF-based dynamic interference spectrogram estimator, and closed-form parameter estimation for the statistical model of the...
The conventional NMF-based speech enhancement algorithm analyzes the magnitude spectrograms of both clean speech and noise in the training data via NMF and estimates a set of spectral basis vectors. These basis vectors are used to span a space to approximate the magnitude spectrogram of the noise-corrupted testing utterances. Finally, the components associated with the clean-speech spectral basis...
Non-negative Matrix Factorization (NMF) is frequently used for audio source separation. One downside of the NMF is, that it is not able to capture temporal structure of sound events. NMF splits these events into different components. In this paper we present an extension to NMF, which is capable of representing sound events with temporal structure in only one component. We also present an algorithm,...
Techniques based on non-negative matrix factorization (NMF) have been successfully used to decompose a spectrogram of a music recording into a dictionary of templates and activations. While advanced NMF variants often yield robust signal models, there are usually some inaccuracies in the factorization since the underlying methods are not prepared for phase cancellations that occur when sounds with...
Social networks contain many information about their authors and extracting these information is one of the important tasks nowadays. Network's weight matrix just shows the relationship between adjacent nodes and cannot show more information about network's structure. On the other hand, clustering and community detection is one of the underlying problems in social networks that network's weight matrix...
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