Model Considerations for Memory-based Automatic Music Transcription

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A00502419" target="_blank" >RIV/49777513:23520/09:00502419 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Model Considerations for Memory-based Automatic Music Transcription
Original language description
The problem of automatic music description is considered. The recorded music is modeled as a superposition of known sounds from a library weighted by unknown weights. Similar observation models are commonly used in statistics and machine learning. Many methods for estimation of the weights are available. These methods differ in the assumptions imposed on the weights. In Bayesian paradigm, these assumptions are typically expressed in the form of prior probability density function (pdf) on the weights. Inthis paper, commonly used assumptions about music signal are summarized and complemented by a new assumption. These assumptions are translated into pdfs and combined into a single prior density using combination of pdfs. Validity of the model is testedin simulation using synthetic data.
Czech name
—
Czech description
—

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Bayesian inference and maximum entropy methods in science and engineering
ISBN
978-0-7354-0729-9
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
—
Publisher name
American Institute of Physics
Place of publication
New York
Event location
Oxford, Mississippi
Event date
Jul 10, 2009
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)