AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Gilberto Bernardes (Autor) FEUP; Davies, MEP (Autor) Outra; Carlos Guedes (Autor) Outra

Ata de Conferência Internacional

Título: 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

Páginas: 316-320

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

New Orleans, LA, MAR 05-09, 2017

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 9 Citações

Outras Informações

ID Authenticus: P-00M-YNB

DOI: 10.1109/icassp.2017.7952169

Abstract (EN): In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles. Results demonstrate the effectiveness of the mode bias in favoring either the major or minor mode, thus allowing users to fine tune this variable to improve correct key estimates on style-specific music datasets or to balance predictions across key modes on unknown input sources.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5