AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Gilberto Bernardes (Author) FEUP; Davies, MEP (Author) Other; Carlos Guedes (Author) Other

Conference proceedings International

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

Pages: 316-320

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

New Orleans, LA, MAR 05-09, 2017

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 9 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5