Robust Real-Time Music Transcription with a Compositional Hierarchical Model

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it su...

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Veröffentlicht in:	PloS one 2017-01, Vol.12 (1), p.e0169411-e0169411
Hauptverfasser:	Pesek, Matevž, Leonardis, Aleš, Marolt, Matija
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Arrangement (Music) Biology and Life Sciences Chords (geometry) Computer and Information Sciences Computer graphics Computer science Data processing Datasets Driving ability Engineering and Technology Frequency estimation Humans Information processing Information retrieval Information science International conferences Laboratories Learning Methods Models, Statistical Models, Theoretical Multimedia Music Neural networks Neurosciences Perception Physical Sciences Pitch Perception Real time Representations Research and Analysis Methods Social Sciences Software Structural hierarchy Transcription Transparency
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