Structured sparsity for automatic music transcription

Sparse representations have previously been applied to the automatic music transcription (AMT) problem. Structured sparsity, such as group and molecular sparsity allows the introduction of prior knowledge to sparse representations. Molecular sparsity has previously been proposed for AMT, however the use of greedy group sparsity has not previously been proposed for this problem. We propose a greedy sparse pursuit based on nearest subspace classification for groups with coherent blocks, based in a non-negative framework, and apply this to AMT. Further to this, we propose an enhanced molecular variant of this group sparse algorithm and demonstrate the effectiveness of this approach.