  Unsupervised disentangled representation learning from the unlabelled audio
data, and high fidelity audio generation have become two linchpins in the
machine learning research fields. However, the representation learned from an
unsupervised setting does not guarantee its' usability for any downstream task
at hand, which can be a wastage of the resources, if the training was conducted
for that particular posterior job. Also, during the representation learning, if
the model is highly biased towards the downstream task, it losses its
generalisation capability which directly benefits the downstream job but the
ability to scale it to other related task is lost. Therefore, to fill this gap,
we propose a new autoencoder based model named "Guided Adversarial Autoencoder
(GAAE)", which can learn both post-task-specific representations and the
general representation capturing the factors of variation in the training data
leveraging a small percentage of labelled samples; thus, makes it suitable for
future related tasks. Furthermore, our proposed model can generate audio with
superior quality, which is indistinguishable from the real audio samples.
Hence, with the extensive experimental results, we have demonstrated that by
harnessing the power of the high-fidelity audio generation, the proposed GAAE
model can learn powerful representation from unlabelled dataset leveraging a
fewer percentage of labelled data as supervision/guidance.
