  This paper presents a way of doing large scale audio understanding without
traditional state of the art neural architectures. Ever since the introduction
of deep learning for understanding audio signals in the past decade,
convolutional architectures have been able to achieve state of the art results
surpassing traditional hand-crafted features. In the recent past, there has
been a similar shift away from traditional convolutional and recurrent neural
networks towards purely end-to-end Transformer architectures. We, in this work,
explore an approach, based on Bag-of-Words model. Our approach does not have
any convolutions, recurrence, attention, transformers or other approaches such
as BERT. We utilize micro and macro level clustered vanilla embeddings, and use
a MLP head for classification. We only use feed-forward encoder-decoder models
to get the bottlenecks of spectral envelops, spectral patches and slices as
well as multi-resolution spectra. A classification head (a feed-forward layer),
similar to the approach in SimCLR is trained on a learned representation. Using
simple codes learned on latent representations, we show how we surpass
traditional convolutional neural network architectures, and come strikingly
close to outperforming powerful Transformer architectures. This work hopefully
would pave way for exciting advancements in the field of representation
learning without massive, end-to-end neural architectures.
