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Found 9 papers, 1 papers with code
Simple and Efficient Quantization Techniques for Neural Speech Coding
This discrete representation is typically generated by applying a quantizer to the output of the neural encoder.
End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation
To obtain precise echo estimates, the parameters of the echo canceler, i. e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals.
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis
Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON).
Microphone Utility Estimation in Acoustic Sensor Networks using Single-Channel Signal Features
In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features.
Manifold learning-supported estimation of relative transfer functions for spatial filtering
Many spatial filtering algorithms used for voice capture in, e. g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs).
End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification.
Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model
The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates.
A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter.
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle
Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios.
