  Over the past two decades, CNN architectures have produced compelling models
of sound perception and cognition, learning hierarchical organizations of
features. Analogous to successes in computer vision, audio feature
classification can be optimized for a particular task of interest, over a wide
variety of datasets and labels. In fact similar architectures designed for
image understanding have proven effective for acoustic scene analysis. Here we
propose applying Transformer based architectures without convolutional layers
to raw audio signals. On a standard dataset of Free Sound 50K,comprising of 200
categories, our model outperforms convolutional models to produce state of the
art results. This is significant as unlike in natural language processing and
computer vision, we do not perform unsupervised pre-training for outperforming
convolutional architectures. On the same training set, with respect mean
aver-age precision benchmarks, we show a significant improvement. We further
improve the performance of Transformer architectures by using techniques such
as pooling inspired from convolutional net-work designed in the past few years.
In addition, we also show how multi-rate signal processing ideas inspired from
wavelets, can be applied to the Transformer embeddings to improve the results.
We also show how our models learns a non-linear non constant band-width
filter-bank, which shows an adaptable time frequency front end representation
for the task of audio understanding, different from other tasks e.g. pitch
estimation.
