English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We introduce a novel algorithm for online estimation of acoustic impulseresponses (AIRs) which allows for fast convergence by exploiting priorknowledge about the fundamental structure of AIRs. The proposed method assumesthat the variability of AIRs of an acoustic scene is confined to alow-dimensional manifold which is embedded in a high-dimensional space ofpossible AIR estimates. We discuss various approaches to locally approximatethe AIR manifold by affine subspaces which are assumed to be tangentialhyperplanes to the manifold. The validity of these model assumptions isverified for simulated data. Subsequently, we describe how the learned modelscan be used to improve online AIR estimates by projecting them onto anadaptively estimated subspace. The parameters determining the subspace arelearned from training samples in a local neighbourhood to the current AIRestimate. This allows the system identification algorithm to benefit frompreceding estimates in the acoustic scene. To assess the proximity of trainingdata AIRs to the current AIR estimate, we introduce a probabilistic extensionof the Euclidean distance which improves the performance for applications withcorrelated excitation signals. Furthermore, we describe how model imperfectionscan be tackled by a soft projection of the AIR estimates. The proposedalgorithm exhibits significantly faster convergence properties in comparison toa high-performance state-of-the-art algorithm. Furthermore, we show an improvedsteady-state performance for speech-excited system identification scenariossuffering from high-level interfering noise and nonunique solutions.

Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model