Improvements of Continuous Model for Memory-based Automatic Music Transcription

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00347257" target="_blank" >RIV/67985556:_____/10:00347257 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Improvements of Continuous Model for Memory-based Automatic Music Transcription

Popis výsledku v původním jazyce

Automatic music transcription is a process recovering the most likely combination of sounds that produced the recorded audio signal. We are concerned with memory-based approach, where the observed signal is modeled as a superposition of sounds from a library. Moreover, we assume that only parts of the sounds can be played. The number of possible combinations is excessive and exact estimation is computationally prohibitive. We propose to transform the original discrete-event model into a less restrictedparametrization and impose the constraints in a soft way via prior information. The resulting model is a non-linear state-space model with Gaussian disturbances. The posterior estimates are evaluated by the extended Kalman filter. Performance of the model is studied in simulation and it is shown that it outperforms previously published methods.

Název v anglickém jazyce

Improvements of Continuous Model for Memory-based Automatic Music Transcription

Popis výsledku anglicky

Automatic music transcription is a process recovering the most likely combination of sounds that produced the recorded audio signal. We are concerned with memory-based approach, where the observed signal is modeled as a superposition of sounds from a library. Moreover, we assume that only parts of the sounds can be played. The number of possible combinations is excessive and exact estimation is computationally prohibitive. We propose to transform the original discrete-event model into a less restrictedparametrization and impose the constraints in a soft way via prior information. The resulting model is a non-linear state-space model with Gaussian disturbances. The posterior estimates are evaluated by the extended Kalman filter. Performance of the model is studied in simulation and it is shown that it outperforms previously published methods.

Druh

D - Stať ve sborníku

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

<a href="/cs/project/GP102%2F08%2FP250" target="_blank" >GP102/08/P250: Metody sekvenčního vzorkování pro identifikaci a řízení distribuovaných systémů</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 18th European signal processing conference

ISBN

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ISSN

2076-1465

e-ISSN

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Počet stran výsledku

5

Strana od-do

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Název nakladatele

Eurasip

Místo vydání

Aalborg

Místo konání akce

Aalborg

Datum konání akce

23. 7. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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