Estimation of Multiple Pitches in Stereophonic Mixtures using a Codebook-based Approach

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceeding

Abstract

In this paper, a method for multi-pitch estimation of stereophonic mixtures of multiple harmonic signals is presented. The method is based on a signal model which takes the amplitude and delay panning parameters of the sources in a stereophonic mixture into account. Furthermore, the method is based on the extended invariance principle (EXIP), and a codebook of realistic amplitude vectors. For each fundamental frequency candidate in each of the sources, the amplitude estimates are mapped to entries in the codebook, and the pitch and model order are estimated jointly. The performance of the proposed method is evaluated using mixtures of real signals. Experiments show an increase in performance when knowledge about the panning parameters is utilized together with the codebook of magnitude amplitudes when compared to a state-of-the-art transcription method.
Close

Details

In this paper, a method for multi-pitch estimation of stereophonic mixtures of multiple harmonic signals is presented. The method is based on a signal model which takes the amplitude and delay panning parameters of the sources in a stereophonic mixture into account. Furthermore, the method is based on the extended invariance principle (EXIP), and a codebook of realistic amplitude vectors. For each fundamental frequency candidate in each of the sources, the amplitude estimates are mapped to entries in the codebook, and the pitch and model order are estimated jointly. The performance of the proposed method is evaluated using mixtures of real signals. Experiments show an increase in performance when knowledge about the panning parameters is utilized together with the codebook of magnitude amplitudes when compared to a state-of-the-art transcription method.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
PublisherIEEE
Publication date2017
Pages186-190
ISBN (Electronic)978-1-5090-4117-6
DOI
StatePublished - 2017
Publication categoryResearch
Peer-reviewedYes
EventThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017
http://www.ieee-icassp2017.org/
http://www.ieee-icassp2017.org/

Conference

ConferenceThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing
LandUnited States
ByNew Orleans
Periode05/03/201709/03/2017
Internetadresse
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Map

ID: 270979209