Structured Sparsity Using Backwards Elimination For Automatic Music Transcription
Ken O'hanlon, Nicolas Keriven, Mark D. Plumbley

Musical signals can be thought of as being sparse and structured, with few elements active at a given instant and temporal continuity of active elements observed.
Greedy algorithms such as Orthogonal Matching Pursuit (OMP), and structured variants, have previously been proposed for Automatic Music Transcription (AMT), however some problems have been noted.
Hence, we propose the use of a backwards elimination strategy in order to perform sparse decompositions for AMT, in particular with a proposed alternative sparse cost function.
However, the main advantage of this approach is the ease with which structure can be incorporated.
The use of group sparsity is shown to give increased AMT performance, while a molecular method incorporating onset information is seen to provide further improvements with little computational effort.