Recently, lots of algorithms using machine learning approaches have been proposed in the speech enhancement area. One of the most well-known approaches is the non-negative matrix factorization (NMF) -based one which analyzes noisy speech with speech and noise bases. However, NMF-based algorithms have difficulties in estimating speech and noise encoding vectors when their subspaces overlap. In t...

Non-negative matrix factorization (NMF) is a fundamental theory that has received much attention and widely used in image engineering, pattern recognition other fields. However, the classical NMF limitations such as only focusing on local information, sensitivity to noise small sample size (SSS) problems. Therefore, how develop improve performance robustness of algorithm worthy challenge. Based...

We consider the stochastic contextual bandit problem with a large number of observed contexts and arms, but with a latent low-dimensional structure across contexts. This low dimensional (latent) structure encodes the fact that both the observed contexts and the mean rewards from the arms are convex mixtures of a small number of underlying latent contexts. At each time, we are presented with an ...

The development of information technology is increasingly rapid, such as social media, which has much influence. Social media a place or used to express and various opinions on topic. One example Instagram. Instagram platform with many features, posting photos, videos, comments, likes, others. comments feature that contained public opinion can be data. Nothing but the post SMB Telkom University...

Non-negative matrix factorization (NMF), with the constraints of non-negativity, has been recently proposed for multi-variate data analysis. Because it allows only additive, not subtractive, combinations of the original data, NMF is capable of producing region or parts-based representation of objects. It has been used for image analysis and text processing. Unlike PCA, the resolutions of NMF ar...

Abstract In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the integration of HMM, temporal dynamics information signals can be taken into account. This includes training stage an stage. stage, sum Poisson distribution, leading to KL ...

Source separation is a widely studied problems in signal processing. Despite the permanent progress reported in the literature it is still considered a significant challenge. This chapter first reviews the use of non-negative matrix factorization (NMF) algorithms for solving source separation problems, and proposes a new way for the supervised training in NMF. Matrix factorization methods have ...

1 Abstract— Powerful modern access to huge amounts of various data having high or low level of privacy brings out a concurrent increasing demand for preserving data privacy. The challenge is how to protect attribute values without jeopardizing the similarity between data objects under analysis. In this paper, we further our previous work on applying matrix decomposition techniques to protect pr...

We investigate a semi-automated identification of technical problems occurred by armed forces weapon systems during mission of war. The proposed methodology is based on a semantic analysis of textual information in reports from soldiers (war logs). Latent semantic indexing (LSI) with non-negative matrix factorization (NMF) as technique from multivariate analysis and linear algebra is used to ex...

For monaural source separation two main approaches have thus far been adopted. One approach involves applying non-negative matrix factorization (NMF) to an observed magnitude spectrogram, interpreted as a non-negative matrix. The other approach is based on the concept of computational auditory scene analysis (CASA). A CASAbased approach called the “harmonic-temporal clustering (HTC)” aims to cl...