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Audio perception is a key to solving a variety of problems ranging fromacoustic scene analysis, music meta-data extraction, recommendation, synthesisand analysis. It can potentially also augment computers in doing tasks thathumans do effortlessly in day-to-day activities. This paper builds upon keyideas to build perception of touch sounds without access to any ground-truthdata. We show how we can leverage ideas from classical signal processing to getlarge amounts of data of any sound of interest with a high precision. Thesesounds are then used, along with the images to map the sounds to a clusteredspace of the latent representation of these images. This approach, not onlyallows us to learn semantic representation of the possible sounds of interest,but also allows association of different modalities to the learneddistinctions. The model trained to map sounds to this clustered representation,gives reasonable performance as opposed to expensive methods collecting a lotof human annotated data. Such approaches can be used to build a state of artperceptual model for any sound of interest described using a few signalprocessing features. Daisy chaining high precision sound event detectors usingsignal processing combined with neural architectures and high dimensionalclustering of unlabelled data is a vastly powerful idea, and can be explored ina variety of ways in future.

Unsupervised Learning of Audio Perception for Robotics Applications: Learning to Project Data to T-SNE/UMAP space