The Generation power of Generative Adversarial Neural Networks (GANs) has shown great promise to learn representations from unlabelled data while guided by a small amount of labelled data. We aim to utilise the generation power of GANs to learn Audio Representations. Most existing studies are, however, focused on images. Some studies use GANs for speech generation, but they are conditioned on text or acoustic features, limiting their use for other audio, such as instruments, and even for speech where transcripts are limited. This paper proposes a novel GAN-based model that we named Guided Generative Adversarial Neural Network (GGAN), which can learn powerful representations and generate good-quality samples using a small amount of labelled data as guidance. Experimental results based on a speech [Speech Command Dataset (S09)] and a non-speech [Musical Instrument Sound dataset (Nsyth)] dataset demonstrate that using only 5% of labelled data as guidance, GGAN learns significantly better representations than the state-of-the-art models.

Generating high-fidelity conditional audio samples and learning representation from unlabelled audio data are two challenging problems in machine learning research. Recent advances in the Generative Adversarial Neural Networks (GAN) architectures show great promise in addressing these challenges. To learn powerful representation using GAN architecture, it requires superior sample generation quality, which requires an enormous amount of labelled data. In this paper, we address this issue by proposing Guided Adversarial Autoencoder (GAAE), which can generate superior conditional audio samples from unlabelled audio data using a small percentage of labelled data as guidance. Representation learned from unlabelled data without any supervision does not guarantee its' usability for any downstream task. On the other hand, during the representation learning, if the model is highly biased towards the downstream task, it losses its generalisation capability. This makes the learned representation hardly useful for any other tasks that are not related to that downstream task. The proposed GAAE model also address these issues. Using this superior conditional generation, GAAE can learn representation specific to the downstream task. Furthermore, GAAE learns another type of representation capturing the general attributes of the data, which is independent of the downstream task at hand. Experimental results involving the S09 and the NSynth dataset attest the superior performance of GAAE compared to the state-of-the-art alternatives.

Image denoising is always a challenging task in the field of computer vision and image processing. In this paper we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model is consisted of an encoder and a decoder, where encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memory network. In the proposed model, the encoder reads an image and catches the abstraction of that image in a vector, where decoder takes that vector as well as the corrupted image to reconstruct a clean image. We have trained our model on MNIST handwritten digit database after making lower half of every image as black as well as adding noise top of that. After a massive destruction of the images where it is hard for a human to understand the content of those images, our model can retrieve that image with minimal error. Our proposed model has been compared with convolutional encoder-decoder, where our model has performed better at generating missing part of the images than convolutional auto encoder.

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