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In this paper, we present a framework for contrastive learning for audiorepresentations, in a self supervised frame work without access to any groundtruth labels. The core idea in self supervised contrastive learning is to mapan audio signal and its various augmented versions (representative of salientaspects of audio like pitch, timbre etc.) to a space where they are closetogether, and are separated from other different signals. In addition we alsoexplore generative models based on state of the art transformer basedarchitectures for learning latent spaces for audio signals, without access toany labels. Here, we map audio signals on a smaller scale to discretedictionary elements and train transformers to predict the next dictionaryelement. We only use data as a method of supervision, bypassing the need oflabels needed to act as a supervision for training the deep neural networks. Wethen use a linear classifier head in order to evaluate the performance of ourmodels, for both self supervised contrastive and generative transformer basedrepresentations that are learned. Our system achieves considerable performance,compared to a fully supervised method, with access to ground truth labels totrain the neural network model. These representations, with avail-ability oflarge scale audio data show promise in various tasks for audio understandingtasks

A Framework for Generative and Contrastive Learning of Audio Representations