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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. In this 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 tested in simulation using synthetic data.
  • 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. In this 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 tested in simulation using synthetic data. (en)
Title
  • Model Considerations for Memory-based Automatic Music Transcription
  • Model Considerations for Memory-based Automatic Music Transcription (en)
skos:prefLabel
  • Model Considerations for Memory-based Automatic Music Transcription
  • Model Considerations for Memory-based Automatic Music Transcription (en)
skos:notation
  • RIV/49777513:23520/09:00502419!RIV10-MSM-23520___
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  • S
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  • 326652
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  • RIV/49777513:23520/09:00502419
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  • automatic music recognition; stochastic modeling; parameter estimation (en)
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  • [6051F903762D]
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  • Oxford, Mississippi
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  • New York
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  • Bayesian inference and maximum entropy methods in science and engineering
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  • Šmídl, Václav
  • Albrecht, Štěpán
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • American Institute of Physics
https://schema.org/isbn
  • 978-0-7354-0729-9
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  • 23520
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