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Given a .WAV file, this experiment extracts the melspectogram of the audio in order to classify the speaker within the sound. For classification, this experiment compares the performance of the ShallowNet and VGG16 frameworks

Python 100.00%
ai cnn nlp shallownet speech vgg16

speaker-classification-with-cnns's Introduction

Hi There ๐Ÿ‘‹

I'm a MSc in Artificial Intelligence student at the University of Malta

Recently I submitted and graduated with my thesis titled 'Mobile Gait Analysis', also winning Best ICT Project for Social Impact alongside a runners-up spot at Tech.mt's Best ICT Academic Project. Just a small outline, the thesis aims to classify regular and irregular 'gait cycles' using a smart-phone or smart-device. The thesis is now published by the International Journal of Semantic Computing following a presentation at the IEEE BigMM Conference.

Currently I am employed at iGolf.One as an AI Specialist in which I mainly work with Python. But past employments made me proficient in languages such as Javascript (Node and React) and C++

Although my academic inspiration in the AI in Medicine/Health Care bubble, I have experience working with NLP, Computer Vision and Machine/Deep Learning applications. Moreover, more published work includes experience with Dynamic Difficulty within an AI in Education solution.

About Me

I'm a 22 year old student currently reading for my Masters in Artificial Intelligence at the University of Malta. But besides that, I'm an avid cyclist and footballer. I also really enjoy listening to music.

Python Python Python Python


Languages

Jupyter Python Java C++ ReactJS

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VS Code Anaconda Azure

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