- Tytuł:
- EMD-based time-frequency analysis methods of non-stationary audio signals
- Autorzy:
-
Lewandowski, Marcin
Grodzicka, Salomea - Powiązania:
- https://bibliotekanauki.pl/articles/2202413.pdf
- Data publikacji:
- 2022
- Wydawca:
- Politechnika Poznańska. Instytut Mechaniki Stosowanej
- Tematy:
-
empirical mode decomposition
non-stationary audio data
time-frequency analysis
empiryczna metoda dekompozycji
niestacjonarne dane dźwiękowe
analiza czasowo-częstotliwościowa - Opis:
- To ensure that any time series data is appropriately interpreted, it should be analyzed with proper signal processing tools. The most common analysis methods are kernel-based transforms, which use base functions and their modifications to represent time series data. This work discusses an analysis of audio data and two of those transforms - the Fourier transform and the wavelet transform based on a priori assumptions about the signal's linearity and stationarity. In audio engineering, these assumptions are invalid because the statistical parameters of most audio signals change with time and cannot be treated as an output of the LTI system. That is why recent approaches involve decomposition of a signal into different modes in a data-dependent and adaptive way, which may provide advantages over kernel-based transforms. Examples of such methods include empirical mode decomposition (EMD), ensemble EMD (EEMD), variational mode decomposition (VMD), or singular spectrum analysis (SSA). Simulations were performed with speech signal for kernel-based and data-dependent decomposition methods, which revealed that evaluated decomposition methods are promising approaches to analyzing non-stationary audio data.
- Źródło:
-
Vibrations in Physical Systems; 2022, 33, 2; art. no. 2022215
0860-6897 - Pojawia się w:
- Vibrations in Physical Systems
- Dostawca treści:
- Biblioteka Nauki
Artykuł