Large scale wireless acoustic recognition network

Abstract

A wireless acoustic sensor network with a compressed acoustic-language model/acoustic recognition model which is pretrained with a language model contrastive language-audio pretraining which involves two main components: an acoustic encoder and a text encoder which are trained on a large dataset of acoustic features and their textual captions. Inputting acoustic features or language generates embedding vectors. These vectors are linked in a joint latent space. Acoustic classification tasks involve assessing similarity between these embedding vectors. Users interact with the model using language input for classification. The architecture of this model permits the segregation of the pre-trained framework into distinct acoustic and text encoders, enabling their deployment across various devices, for instance, positioning the acoustic encoder on edge nodes and the text encoder on a central node.

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