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Found 24 papers, 5 papers with code
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.
Novel features for the detection of bearing faults in railway vehicles
However, these features are usually evaluated on data originating from relatively simple scenarios and a significant performance loss can be observed if more realistic scenarios are considered.
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge
In this article, we present our contribution to the ICPHM 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Analysis.
Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data
In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation.
Localizing Spatial Information in Neural Spatiospectral Filters
Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene.
Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction
In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially.
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).
Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction
We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends.
Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering
We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF).
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).
Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement
As shown by the experimental results, the proposed approach is able to exploit both spatial and spectral characteristics of the desired source signal resulting in a physically plausible spatial selectivity and superior speech quality compared to other baseline methods.
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.
Extrapolation of Bandlimited Multidimensional Signals from Continuous Measurements
In particular, we propose an iterative method to reconstruct bandlimited multidimensional signals based on truncated versions of the original signal to bounded regions---herein referred to as continuous measurements.
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.
The LOCATA Challenge: Acoustic Source Localization and Tracking
The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking.
An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
In this paper, we advance a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network - hidden Markov model) hybrid systems.
Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies
In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification.
Coherent-to-Diffuse Power Ratio Estimation for Dereverberation
Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation.
Sound
The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective
In this article, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective.
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
