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Method for Clustering of Brain Activity Data Derived from EEG Signals

100%

Kurowski A. , Mrozik K. , Kostek B. , Czyzewski A.

Fundamenta Informaticae

2019

tom Vol. 168, nr 2-4

249--268

A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets, Daubechies and Symlets. Euclidean distance between clusters normalized with respect to the standard deviation of the whole set of data are used to separate each task performed by participants. The results of this stage allow for an assessment of separability between subsets of data associated with each activity performed by experiment participants. The speed of convergence of the training process employing deep learning-based clustering is also measured.

Speech sound detection employing deep learning

75%

Polak C. , Mańkowski J. , Uciński W. , Schramka P. , Mysiakowski M. , Kurowski A.

Annals of Computer Science and Information Systems

2021

tom Vol. 26

221--222

The primary way of communication between people is speech, both in the form of everyday conversation and speech signal transmitted and recorded in numerous ways. The latter example is especially important in the modern days of the global SARS-CoV-2 pandemic when it is often not possible to meet with people and talk with them in person. Streaming, VoIP calls, live podcasts are just some of the many applications that have seen a significant increase in usage due to the necessity of social distancing. In our paper, we provide a method to design, develop, and test the deep learning-based algorithm capable of performing voice activity detection in a manner better than other benchmark solutions like the WebRTC VAD algorithm, which is an industry standard based mainly on a classic approach to speech signal processing.