  Solving tasks such as speaker recognition, music classification, or semantic
audio event tagging with deep learning models typically requires
computationally demanding networks. General-purpose audio embeddings (GPAEs)
are dense representations of audio signals that allow lightweight, shallow
classifiers to tackle various audio tasks. The idea is that a single complex
feature extractor would extract dense GPAEs, while shallow MLPs can produce
task-specific predictions. If the extracted dense representations are general
enough to allow the simple downstream classifiers to generalize to a variety of
tasks in the audio domain, a single costly forward pass suffices to solve
multiple tasks in parallel. In this work, we try to reduce the cost of GPAE
extractors to make them suitable for resource-constrained devices. We use
efficient MobileNets trained on AudioSet using Knowledge Distillation from a
Transformer ensemble as efficient GPAE extractors. We explore how to obtain
high-quality GPAEs from the model, study how model complexity relates to the
quality of extracted GPAEs, and conclude that low-complexity models can
generate competitive GPAEs, paving the way for analyzing audio streams on edge
devices w.r.t. multiple audio classification and recognition tasks.
