Adaptive Approach For Sparse Representations Using The Locally Competitive Algorithm For Audio
This is an incremental improvement for real-time audio processing applications, addressing a specific bottleneck in computational demand.
The paper tackles the computational inefficiency of combining a gammachirp filterbank with Matching Pursuit for sparse audio representations by proposing an adaptive method using the Locally Competitive Algorithm (LCA) and backpropagation, resulting in improved sparsity, reconstruction quality, and convergence time.
Gammachirp filterbank has been used to approximate the cochlea in sparse coding algorithms. An oriented grid search optimization was applied to adapt the gammachirp's parameters and improve the Matching Pursuit (MP) algorithm's sparsity along with the reconstruction quality. However, this combination of a greedy algorithm with a grid search at each iteration is computationally demanding and not suitable for real-time applications. This paper presents an adaptive approach to optimize the gammachirp's parameters but in the context of the Locally Competitive Algorithm (LCA) that requires much fewer computations than MP. The proposed method consists of taking advantage of the LCA's neural architecture to automatically adapt the gammachirp's filterbank using the backpropagation algorithm. Results demonstrate an improvement in the LCA's performance with our approach in terms of sparsity, reconstruction quality, and convergence time. This approach can yield a significant advantage over existing approaches for real-time applications.