Dennis L. Sun

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Supervised and semi-supervised source separation algorithms based on non-negative matrix factorization have been shown to be quite effective. However, they require isolated training examples of one or more sources, which is often difficult to obtain. This limits the practical applicability of these algorithms. We examine the problem of efficiently utilizing(More)
We develop a framework for post-selection inference with the lasso. At the core of our framework is a result that characterizes the exact (non-asymptotic) distribution of linear combinations/contrasts of truncated normal random variables. This result allows us to (i) obtain honest confidence intervals for the selected coefficients that account for the(More)
Non-negative matrix factorization (NMF) is a popular method for learning interpretable features from non-negative data, such as counts or magnitudes. Different cost functions are used with NMF in different applications. We develop an algorithm, based on the alternating direction method of multipliers, that tackles NMF problems whose cost function is a(More)
Vol. 44, No. 3—June 2016 Articles Exact post-selection inference, with application to the lasso JASON D. LEE, DENNIS L. SUN, YUEKAI SUN AND JONATHAN E. TAYLOR 907 Nonparametric eigenvalue-regularized precision or covariance matrix estimator CLIFFORD LAM 928 Global rates of convergence of the MLEs of log-concave and s-concave densities CHARLES R. DOSS AND(More)
Voice activity detection (VAD) in the presence of heavy, non-stationary noise is a challenging problem that has attracted attention in recent years. Most modern VAD systems require training on highly specialized data: either labeled mixtures of speech and noise that are matched to the application, or, at the very least, noise data similar to that(More)
Bandwidth extension is the problem of recovering missing bandwidth in audio signals that have been band-passed, typically for compression purposes. One approach that has been shown to be successful for bandwidth extension is non-negative matrix factorization (NMF). The disadvantage of NMF is that it is non-convex and intractable to solve in general.(More)
The problem of recovering a signal from the magnitude of its short-time Fourier transform (STFT) is a longstanding one in audio signal processing. Existing approaches rely on heuristics which often perform poorly because of the nonconvexity of the problem. We introduce a formulation of the problem that lends itself to a tractable convex program. We observe(More)
A coupled volume-of-fluid and level set (VOSET) method, which combines the advantages and overcomes the disadvantages of VOF and LS methods, is presented for computing incompressible two-phase flows. In this method VOF method is used to capture interfaces, which can conserve the mass and overcome the disadvantage of nonconservation of mass in LS method. An(More)
Musical mixtures can be modeled as being composed of two characteristic sources: singing voice and background music. Many music/voice separation techniques tend to focus on modeling one source; the residual is then used to explain the other source. In such cases, separation performance is often unsatisfactory for the source that has not been explicitly(More)
In applications such as speech and audio denoising, music transcription, music and audio based forensics, it is desirable to decompose a single-channel recording into its respective sources, commonly referred to as blind source separation (BSS). One of the techniques used in BSS is non-negative matrix factorization (NMF). In NMF both supervised and(More)