  In this paper, we propose an efficient MLP-based approach for learning audio
representations, namely timestamp and scene-level audio embeddings. We use an
encoder consisting of sequentially stacked gated MLP blocks, which accept 2D
MFCCs as inputs. In addition, we also provide a simple temporal
interpolation-based algorithm for computing scene-level embeddings from
timestamp embeddings. The audio representations generated by our method are
evaluated across a diverse set of benchmarks at the Holistic Evaluation of
Audio Representations (HEAR) challenge, hosted at the NeurIPS 2021 competition
track. We achieved first place on the Speech Commands (full), Speech Commands
(5 hours), and the Mridingham Tonic benchmarks. Furthermore, our approach is
also the most resource-efficient among all the submitted methods, in terms of
both the number of model parameters and the time required to compute
embeddings.
