Neil-Shah

GANs-for-Speech-Enhancement

Generative Adversarial Network implemented for the Time-Frequency based Speech Enhancement

Classification-IRIS-

Classification results on IRIS dataset is reported using 10-fold cross validation. Among 150 examples, some flowers are miss-classified as can be understood from the accuracy results. The code is implemented on matlab.

fairseq-exps

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

mnist-CNN

Trains CNN for MNIST dataset. Once the weights and activations are learned, the summaries gets created in LOGDIR path (mentioned in code), and can be visualized on Tensorboard.

Regression-linear-nonlinear

A single layer neural network with two input neurons is trained for predicting a linear equation y=ax+b., and a two layer neural network with 4 input neurons, 9 hidden neurons and 3 output neurons is trained for nonlinear prediction 0f given 3 nonlinear equations.

speech-resynthesis-vocoder

An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Tennis-Refactoring-Kata

Starting code for a Refactoring Code Kata on the Tennis rules

tts

P2E

StethoSpeech

Neil-Shah/GANs-for-Speech-Enhancement

Generative Adversarial Network implemented for the Time-Frequency based Speech Enhancement

Neil-Shah/fairseq-exps

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Neil-Shah/Tennis-Refactoring-Kata

Starting code for a Refactoring Code Kata on the Tennis rules

Neil-Shah/speech-resynthesis-vocoder

An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Neil-Shah/Regression-linear-nonlinear

A single layer neural network with two input neurons is trained for predicting a linear equation y=ax+b., and a two layer neural network with 4 input neurons, 9 hidden neurons and 3 output neurons is trained for nonlinear prediction 0f given 3 nonlinear equations.

Neil-Shah/Classification-IRIS-

Classification results on IRIS dataset is reported using 10-fold cross validation. Among 150 examples, some flowers are miss-classified as can be understood from the accuracy results. The code is implemented on matlab.

Neil-Shah/mnist-CNN

Trains CNN for MNIST dataset. Once the weights and activations are learned, the summaries gets created in LOGDIR path (mentioned in code), and can be visualized on Tensorboard.