16 accepted papers
This paper presents the Deep learning-based Perceptual Audio Quality metric (DeePAQ) for evaluating general audio quality. Our approach leverages metric learning together with the music foundation model MERT, guided by surrogate labels, to construct an embedding space that captures distortion intens…
Online blind source separation is essential for both speech communication and human-machine interaction. Among existing approaches, overdetermined independent vector analysis (OverIVA) delivers strong performance by exploiting the statistical independence of source signals and the orthogonality betw…
Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discri…
Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the perform…
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of th…
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). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustical…
Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which al…
Many algorithms for speech dereverberation and noise reduction rely on an estimate of the coherent-to-diffuse power ratio (CDR). Such systems typically operate in very diverse acoustic conditions, and CDR estimators relying on very weak model assumptions about the acoustic sound field of the desired…
In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic environment. In particular, we look at the scenario when a node in the ASN is displaced from its position during training.…
To address the vital problem of selecting the most useful microphones in wireless acoustic sensor networks, this paper proposes a novel, general-purpose approach that accounts for both acoustic and network aspects and remains application-agnostic for broad applicability. The inter-channel correlatio…
We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noi…
This contribution presents a novel approach for coherence-based signal enhancement. An estimator for the coherent-to-diffuse ratio (CDR) is devised, which exploits the concept of generalized magnitude coherence and thus, unlike common state-of-the-art schemes, can simultaneously take advantage of mo…
We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm for blind source separation incorporating an additional spatial prior over the demixing matrices. In this way, the outer permutation ambiguity of IVA is avoided and the algorithm can be guided towar…
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability information to obtain robust estimation results in adverse acoustic scenarios and estimating…
In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density is then incorporated into the Gaussian particle fil…
We propose a method for estimating the distance between a sound source and a pair of recording microphones. The developed algorithm operates in the short-time Fourier transform domain and is based on estimates of the coherent-to-diffuse power ratio, which provides a measure for the amount of reverbe…