Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Beta-Divergence-Based Recurrence Plots for Audio Time-Series Analysis

Urheber*innen

Deckert,  Elena
External Organizations;

Dreesen,  Philippe
External Organizations;

/persons/resource/Marwan

Marwan,  Norbert       
Potsdam Institute for Climate Impact Research;

Boussé,  Martijn
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Deckert, E., Dreesen, P., Marwan, N., Boussé, M. (2025): Beta-Divergence-Based Recurrence Plots for Audio Time-Series Analysis - 33rd European Signal Processing Conference (EUSIPCO): Proceedings, 33rd European Signal Processing Conference (EUSIPCO) 2025 (Palermo/Italy 2025), 291-295, 5 p.
https://doi.org/10.23919/EUSIPCO63237.2025.11226301


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_34377
Zusammenfassung
While recurrence plots (RPs) are a well-known tool in various fields such as physics, astronomy, and health sciences, their application in audio signal processing remains limited. RPs are a data-analysis tool that visualizes recurrences of states, which are typically measured by the Euclidean norm. When analyzing audio data, however, β-divergences are more common than the Euclidean norm because of their adaptability and suitability for audio-specific characteristics. Therefore, we propose the use of β-divergence-based RPs to gain additional insight into audio data. In this paper, we explore the properties of such RPs, providing a fundamental understanding of their characteristics and an indication of possible future applications. Our findings show that β-divergence-based RPs can provide additional information over traditional RPs, making them well-suited for audio analysis.