Konrad Kowalczyk — accepted AI-conference papers

2025

Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio

Mateusz Baranski, Jan Jasinski, Julitta Bartolewska, Stanislaw Kacprzak, Marcin Witkowski, Konrad Kowalczyk

ICASSP 2025accepted

Hallucinations of deep neural models are amongst key challenges in automatic speech recognition (ASR). In this paper, we investigate hallucinations of the Whisper ASR model induced by non-speech audio segments present during inference. By inducting hallucinations with various types of sounds, we sho…

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2023

Convolutive NTF for Ambisonic Source Separation under Reverberant Conditions

Mateusz Guzik, Konrad Kowalczyk

ICASSP 2023accepted

This paper presents a Non-negative Tensor Factorization (NTF) based sound source separation method with a novel convolutive Spatial Covariance Matrix (SCM) model, that is suitable for use with reverberant Ambisonic signals. The presented solution builds upon a previous work on SHD SCM-based NTF, but…

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2022

Convolutional Weighted Minimum Mean Square Error Filter for Joint Source Separation and Dereverberation

Mieszko Fras, Marcin Witkowski, Konrad Kowalczyk

ICASSP 2022accepted

Practical scenarios with multiple simultaneously active speakers recorded using one or more microphones in reverberant rooms pose a challenging problem when the extraction of the desired speaker signal is sought for. The majority of techniques found in the literature facilitate either source separat…

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2022

Spoken Language Recognition with Cluster-Based Modeling

Stanislaw Kacprzak, Magdalena Rybicka, Konrad Kowalczyk

ICASSP 2022accepted

In this study, we analyze the incorporation of cluster-based modeling into the language recognition systems, in which a single utterance is represented as an embedding, deploying widely used i-vectors and x-vectors. We compare the results obtained with a Cosine Distance Scoring, Gaussian Mixture Mod…

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2022

Wishart Localization Prior On Spatial Covariance Matrix In Ambisonic Source Separation Using Non-Negative Tensor Factorization

Mateusz Guzik, Konrad Kowalczyk

ICASSP 2022accepted

This paper presents an extension of the existing Non-negative Tensor Factorization (NTF) based method for sound source separation under reverberant conditions, formulated for Ambisonic microphone mixture signals. In particular, we address the problem of optimal exploitation of the prior knowledge co…

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2021

Maximum a Posteriori Estimator for Convolutive Sound Source Separation with Sub-Source Based NTF Model and the Localization Probabilistic Prior on the Mixing Matrix

Mieszko Fras, Konrad Kowalczyk

ICASSP 2021accepted

In this paper we present a method for the separation of sound source signals recorded using multiple microphones in a reverberant room. In particular, we propose a maximum a posteriori (MAP) estimator based on the multichannel nonnegative tensor factorization (NTF) model with the localization prior…

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2020

Exploiting Rays in Blind Localization of Distributed Sensor Arrays

Szymon Wozniak, Konrad Kowalczyk

ICASSP 2020accepted

Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i.e. the setup geometry up to a scaling factor. Fir…

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2015

Residual noise control using a parametric multichannel Wiener filter

Sebastian Braun, Konrad Kowalczyk, Emanuël A. P. Habets

ICASSP 2015accepted

Multichannel noise reduction techniques are commonly used in speech communication applications. In these applications, it is often desired to maintain a residual amount of background noise to avoid perceptually unpleasant artifacts, such as musical tones or time periods of complete silence. Noise re…

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