Rainer Martin

Alexandru Nelus, Rene Glitza, Rainer Martin

In this paper we introduce a realistic and challenging, multi-source and multi-room acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks. Our proposed clustering method is based on a single microphone per node and on unsupervised clustered federated learning which employs a light-weight autoencoder model. We present an improved clustering control strategy that takes into account the variability of the acoustic scene and allows the estimation of a dynamic range of clusters using reduced amounts of training data. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.

Alexandru Nelus, Rene Glitza, Rainer Martin

In this paper we present a privacy-aware method for estimating source-dominated microphone clusters in the context of acoustic sensor networks (ASNs). The approach is based on clustered federated learning which we adapt to unsupervised scenarios by employing a light-weight autoencoder model. The model is further optimized for training on very scarce data. In order to best harness the benefits of clustered microphone nodes in ASN applications, a method for the computation of cluster membership values is introduced. We validate the performance of the proposed approach using clustering-based measures and a network-wide classification task.

Semih Ağcaer, Rainer Martin

In this work we apply Amplitude Modulation Spectrum (AMS) features to the source localization problem. Our approach computes 36 bilateral features for 2s long signal segments and estimates the azimuthal directions of a sound source through a binaurally trained classifier. This directional information of a sound source could be e.g. used to steer the beamformer in a hearing aid to the source of interest in order to increase the SNR. We evaluated our approach on the development set of the IEEE-AASP Challenge on sound source localization and tracking (LOCATA) and achieved a 4.25° smaller MAE than the baseline approach. Additionally, our approach is computationally less complex.