An empirical comparison of latent adaptive filters for acoustic system identification and tracking

In real-time spatial audio algorithms, adaptive filters are employed to learn and track spatial and acoustic filters. When the space of possible filter weights is close to a low-dimensional manifold, we can improve the convergence rate of adaptive filters by adapting along this manifold. This can be achieved using latent adaptive filters, which constrain the weights to remain within the range space of an auto-encoder's decoder by updating in its latent space. Although previous studies have explored various latent adaptive filters for acoustic system identification, a comparison of the convergence rates among different latent adaptive filter structures has not yet been conducted. Additionally, it is well-established that traditional adaptive filters often struggle to track changes in acoustic impulse responses caused by the continuous movement of the source and receiver. However, the tracking performance of latent adaptive filters has not been investigated. In this study, we empirically evaluate the performance of acoustic impulse response identification and tracking across different variants of latent adaptive filters.