Search Results for author:
Found 12 papers, 3 papers with code
Infrastructure-less Localization from Indoor Environmental Sounds Based on Spectral Decomposition and Spatial Likelihood Model
The proposed method can be used to evaluate the spatial likelihood from environmental sounds.
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
Our neural separation model introduced for AVI alternately performs neural network blocks and single steps of an efficient iterative algorithm called iterative source steering.
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF
Our DNN-free system leverages the posteriors of the latest source spectrograms given by block-online FastMNMF to derive the current source covariance matrices for frame-online beamforming.
Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments
This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conversations made in real noisy echoic environments (e. g., cocktail party).
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments.
Ranked #1 on
Speech Enhancement
on EasyCom
(SDR metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation
This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) from a unified point of view.
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.
Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior
To solve this problem, we replace a low-rank speech model with a deep generative speech model, i. e., formulate a probabilistic model of noisy speech by integrating a deep speech model, a low-rank noise model, and a full-rank or rank-1 model of spatial characteristics of speech and noise.
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals.
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition
To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
A popular approach to multichannel source separation is to integrate a spatial model with a source model for estimating the spatial covariance matrices (SCMs) and power spectral densities (PSDs) of each sound source in the time-frequency domain.
Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech.
