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.

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... nally, for some regimes, the sparsity level approaches those of Matching Pursuit while still maintaining signal integrity.