Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

Speaker embeddings, from the ECAPA-TDNN speaker verification network, were recently introduced as features for the task of clustering microphones in ad hoc arrays. Our previous work demonstrated that, in comparison to signal-based Mod-MFCC features, using speaker embeddings yielded a more robust and logical clustering of the microphones around the sources of interest. This work aims to further establish speaker embeddings as a robust feature for ad hoc microphone clustering by addressing open and additional questions of practical interest, arising from our prior work. Specifically, whereas our initial work made use of simulated data based on shoe-box acoustics models, we now present a more thorough analysis in more realistic settings. Furthermore, we investigate additional important considerations such as the choice of the distance metric used in the fuzzy C-means clustering; the minimal time range across which data need to be aggregated to obtain robust clusters; and the performance of the features in increasingly more challenging situations, and with multiple speakers. We also contrast the results on the basis of several metrics for quantifying the quality of such ad hoc clusters. Results indicate that the speaker embeddings are robust to short inference times, and deliver logical and useful clusters, even when the sources are very close to each other.

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Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

Cited by 5 publications

References 19 publications

Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder

Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder

Mobile_FL: A streamlined FL framework for process optimisation via client clustering using rough c-means algorithm

Robustness of ad hoc microphone clustering using speaker embeddings: evaluation under realistic and challenging scenarios

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