About: Automatic music Transcription via Music Component Identification     Goto   Sponge   NotDistinct   Permalink

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  • 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 restricted parametrization 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 in figures that it outperforms a model which does not utilize a priori information.
  • 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 restricted parametrization 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 in figures that it outperforms a model which does not utilize a priori information. (en)
Title
  • Automatic music Transcription via Music Component Identification
  • Automatic music Transcription via Music Component Identification (en)
skos:prefLabel
  • Automatic music Transcription via Music Component Identification
  • Automatic music Transcription via Music Component Identification (en)
skos:notation
  • RIV/49777513:23520/10:00504050!RIV12-GA0-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/07/1191)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 248191
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/10:00504050
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Automatic music transcription, Extended Kalman Filter, geometric merging of probabilities (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [5276521546E2]
http://linked.open...v/mistoKonaniAkce
  • Berlin
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • 36. Deutsche Jahrestagung für Akustik, DAGA 2010
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Albrecht, Štěpán
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Deutsche Gesellschaft für Akustik
https://schema.org/isbn
  • 978-3-9808659-8-2
http://localhost/t...ganizacniJednotka
  • 23520
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