2 citations · 5 across the 7 of their papers we have counts for
7 papers
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes
Vadim Rozenfeld, Bracha Laufer Goldshtein
We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influ…
Risk-Controlling Model Selection via Guided Bayesian Optimization
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is t…
Efficiently Controlling Multiple Risks with Pareto Testing
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hype…
ML Estimation and CRBs for Reverberation, Speech and Noise PSDs in Rank-Deficient Noise-Field
Yaron Laufer, Bracha Laufer-Goldshtein, Sharon Gannot
Speech communication systems are prone to performance degradation in reverberant and noisy acoustic environments. Dereverberation and noise reduction algorithms typically require s…
Data-Driven Source Separation Based on Simplex Analysis
Bracha Laufer-Goldshtein, Ronen Talmon, Sharon Gannot
Blind source separation (BSS) is addressed, using a novel data-driven approach, based on a well-established probabilistic model. The proposed method is specifically designed for se…
Semi-Supervised Source Localization on Multiple-Manifolds with Distributed Microphones
Bracha Laufer-Goldshtein, Ronen Talmon, Sharon Gannot
The problem of source localization with ad hoc microphone networks in noisy and reverberant enclosures, given a training set of prerecorded measurements, is addressed in this paper…
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Bracha Laufer-Goldshtein, Ronen Talmon, Sharon Gannot
Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal…