  Audio perception is a key to solving a variety of problems ranging from
acoustic scene analysis, music meta-data extraction, recommendation, synthesis
and analysis. It can potentially also augment computers in doing tasks that
humans do effortlessly in day-to-day activities. This paper builds upon key
ideas to build perception of touch sounds without access to any ground-truth
data. We show how we can leverage ideas from classical signal processing to get
large amounts of data of any sound of interest with a high precision. These
sounds are then used, along with the images to map the sounds to a clustered
space of the latent representation of these images. This approach, not only
allows us to learn semantic representation of the possible sounds of interest,
but also allows association of different modalities to the learned
distinctions. The model trained to map sounds to this clustered representation,
gives reasonable performance as opposed to expensive methods collecting a lot
of human annotated data. Such approaches can be used to build a state of art
perceptual model for any sound of interest described using a few signal
processing features. Daisy chaining high precision sound event detectors using
signal processing combined with neural architectures and high dimensional
clustering of unlabelled data is a vastly powerful idea, and can be explored in
a variety of ways in future.
