Radoslaw Mazur

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Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an(More)
In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. Transformed to the frequency domain, existing algorithms can efficiently solve the reduction of the source separation problem into independent instantaneous separation in each frequency bin. However, this independency leads to the problem(More)
Virtual 3-D sound can be easily delivered to a listener by binaural audio signals that are reproduced via headphones, which guarantees that only the correct signals reach the corresponding ears. Reproducing the binaural audio signal by two or more loudspeakers introduces the problems of crosstalk on the one hand, and, of reverberation on the other hand. In(More)
In this paper, we propose to use the scaling ambiguity of convolutive blind source separation for shortening the unmixing filters. An often used approach for separating convolutive mixtures is the transformation to the time-frequency domain where an instantaneous ICA algorithm can be applied for each frequency separately. This approach leads to the so(More)
The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, such as in a recognition task. To find out how similar nonspeech signals are to speech, we measure the closeness(More)
Harmonics in a power system caused by highly nonlinear devices degrade its performance. Controlling and reducing such harmonics have been a major concern of power engineers for many years. The power system harmonic analysis is the process of calculating the magnitudes and phases of the fundamental and higher order harmonics of system signals. The frequency(More)
For the separation of convolutive mixtures, an often used approach is the transformation to the time-frequency domain, where the problem is reduced to multiple instantaneous mixtures. This allows for the employment of well-known ICA algorithms. The drawbacks of this method are the inherent permutation and scaling problems. These ambiguities have to be(More)
In this paper we propose a new clustering approach for solving the permutation ambiguity in convolutive blind source separation. After the transformation to the time-frequency domain, the problem of separation of sources can be reduced to multiple instantaneous problems, which may be solved using independent component analysis. The drawbacks of this(More)