English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, we propose a two-step approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (grouping of the components belonging to the same source) using vector clustering. For the signal analysis, two existing algorithms are considered and compared: namely the EMD (empirical mode decomposition) algorithm and a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its validity for both instantaneous and convolutive mixtures and its ability to separate more sources than sensors. Simulation results are given to compare and assess the performance of the proposed algorithms

Underdetermined Blind Audio Source Separation Using Modal Decomposition