Forecasting high-dimensional time series plays a crucial role in many applications such as
demand forecasting and financial predictions. Modern datasets can have millions of …

Time series prediction problems are becoming increasingly high-dimensional in modern
applications, such as climatology and demand forecasting. For example, in the latter …

Reducing the interference noise in a monaural noisy speech signal has been a challenging
task for many years. Compared to traditional unsupervised speech enhancement methods …

Source separation models that make use of nonnegativity in their parameters have been
gaining increasing popularity in the last few years, spawning a significant number of …

In this paper, we propose a novel teacher-student learning framework for the preprocessing
of a speech recognizer, leveraging the online noise tracking capabilities of improved minima …

We propose a multi-objective framework to learn both secondary targets not directly related
to the intended task of speech enhancement (SE) and the primary target of the clean log …

A novel Bayesian matrix factorization method for bounded support data is presented. Each
entry in the observation matrix is assumed to be beta distributed. As the beta distribution has …

We present a semi-supervised source separation methodology to denoise speech by
modeling speech as one source and noise as the other source. We model speech using the …

This letter presents a speech enhancement technique combining statistical models and non-
negative matrix factorization (NMF) with on-line update of speech and noise bases. The …

We have described some of the problems with modeling mixed acoustic signals in the log
spectral domain using graphical models, as well as some current approaches to handling …