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Study of the impact of the pandemic on the functioning of micro-enterprises in the Silesian voivodeship

Michalski Krzysztof

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2022

z. 161

121--135

Purpose: The objective of the paper is to study the impact of the global coronavirus pandemic on the standing of micro-enterprises in the Silesian Voivodeship. Design/methodology/approach: A study sample of 120 micro-enterprises from the Silesian Voivodeship has been selected. The study methods such as an interview and a survey have been used. A questionnaire has been used as a tool. The properly completed surveys have been obtained from 43 enterprises. Findings: It has been established that depending on the specific nature of a given enterprise and the area in which it operates, enterprises are, to varying degrees, susceptible to the impact of adverse factors related to the emergence of the global coronavirus pandemic, which, in turn, results in the differences in the standing of these enterprises. Research limitations/implications: In view of the small study sample, the studies carried out do not create a complete picture of the impact of the pandemic on micro-enterprises in the Silesian Voivodeship. They are rather a contribution to further studies. These should be conducted on the basis of a larger study sample. Originality/value: The global coronavirus pandemic which affected the world in the years 2019-2022 has left a strong mark on many aspects of human functioning, including pursuing business activity. It is important to gain knowledge on the impact of pandemic-related restrictions on the functioning of micro-enterprises in order to develop mechanisms to mitigate the adverse effects on entrepreneurship based on micro-enterprises.

Speech sound detection employing deep learning

Polak Cezary, Mańkowski Jakub, Uciński Wiktor, Schramka Patryk, Mysiakowski Mikołaj, Kurowski Adam

Annals of Computer Science and Information Systems

2021

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.