A causal Locally Competitive Algorithm for the sparse decomposition of audio signals

While current inference methods can decompose audio signals, they require the entire signal upfront and are therefore ill-suited for real-time applications requiring causal processing. We propose a neurally-inspired, causal, sparse inference scheme based on the Locally Competitive Algorithm (LCA) over a temporal-spectral neighborhood. We demonstrate that this causal inference scheme can achieve lower sparsity levels and better signal fidelity than current filter and threshold approaches. Additionally, for some regimes, the sparsity level approaches those of Matching Pursuit while still maintaining signal integrity.