The Effect of Audio Degradation on Onset Detection Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU143898" target="_blank" >RIV/00216305:26220/22:PU143898 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/the-effect-of-audio-degradation-on-onset-detection-systems/" target="_blank" >http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/the-effect-of-audio-degradation-on-onset-detection-systems/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Effect of Audio Degradation on Onset Detection Systems

Popis výsledku v původním jazyce

Although many articles in the field of Music Information Retrieval have been introduced to improve onset detection systems, only the bare minimum focus on the degradation of input audio to increase detection accuracy. This article evaluates the accuracy of five onset detectors, including stateof-the-art machine and non-machine learning-based systems, and compares the influence of various types of audio signal degradation on musical onset detection. We used three different degradations based on impulse responses, Teager–Kaiser energy operator, and two MP3 compression settings. The results suggest that if MP3 compression of any settings is applied, the accuracy of detection systems is very similar. Using the energy operator as degradation has not improved overall detection but may offer the potential of pre-processing the neural network input signal for easier identification of onsets in a training phase. Furthermore, radio broadcast degradation increases the number of all predicted onsets in general, both true and false positives, resulting in better recall but worse precision. This information could be used to modify the pre-processing phase of neural network-based detectors and to optimize the sensitivity trade-off.

Název v anglickém jazyce

The Effect of Audio Degradation on Onset Detection Systems

Popis výsledku anglicky

Although many articles in the field of Music Information Retrieval have been introduced to improve onset detection systems, only the bare minimum focus on the degradation of input audio to increase detection accuracy. This article evaluates the accuracy of five onset detectors, including stateof-the-art machine and non-machine learning-based systems, and compares the influence of various types of audio signal degradation on musical onset detection. We used three different degradations based on impulse responses, Teager–Kaiser energy operator, and two MP3 compression settings. The results suggest that if MP3 compression of any settings is applied, the accuracy of detection systems is very similar. Using the energy operator as degradation has not improved overall detection but may offer the potential of pre-processing the neural network input signal for easier identification of onsets in a training phase. Furthermore, radio broadcast degradation increases the number of all predicted onsets in general, both true and false positives, resulting in better recall but worse precision. This information could be used to modify the pre-processing phase of neural network-based detectors and to optimize the sensitivity trade-off.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)

ISSN

1213-1539

e-ISSN

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Svazek periodika

24

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

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EID výsledku v databázi Scopus

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