Matevž Pesek (Avtor), Aleš Leonardis (Avtor), Matija Marolt (Avtor)

Povzetek

This paper presents a compositional hierarchical model for pattern discovery in symbolic music. The model can be regarded as a deep architecture with a transparent structure. It can learn a set of repeated patterns within individual works or larger corpora in an unsupervised manner, relying on statistics of pattern occurrences, and robustly infer the learned patterns in new, unknown works. A learned model contains representations of patterns on different layers, from the simple short structures on lower layers to the longer and more complex music structures on higher layers. A pattern selection procedure can be used to extract the most frequent patterns from the model. We evaluate the model on the publicly available JKU Patterns Datasets and compare the results to other approaches.

Ključne besede

pridobivanje informacij iz glasbe;kompozicionalno modeliranje;odkrivanje vzorcev;simbolne predstavitve glasbe;music information retrieval;compositional modelling;pattern discovery;symbolic music representations;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
UDK: 004:78
COBISS: 1537631683 Povezava se bo odprla v novem oknu
ISSN: 2076-3417
Št. ogledov: 176
Št. prenosov: 51
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: pridobivanje informacij iz glasbe;kompozicionalno modeliranje;odkrivanje vzorcev;simbolne predstavitve glasbe;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-21
Letnik: ǂVol. ǂ7
Zvezek: ǂiss. ǂ11
Čas izdaje: Nov. 2017
DOI: 10.3390/app7111135
ID: 13505853