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Greetings! My name is Fred. 👋

I hold the position of a professor at the esteemed Faculty of Engineering within the Federal University of Mato Grosso (UFMT), located in Cuiabá-MT, Brazil. My academic journey has led me to the achievement of a Ph.D. in the field of Artificial Intelligence from the Federal University of Goiás (UFG). My primary area of interest revolves around the realm of NLP.

I've taken the initiative to curate several repositories, which can be accessed here. While they may currently appear somewhat unorganized, I am actively planning to arrange them in a more structured manner in the future. For those seeking additional insights, I kindly invite you to explore further through the following links:

Frederico S. Oliveira's Projects

ai-programming-using-python icon ai-programming-using-python

This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach

audio-slicer icon audio-slicer

A simple GUI application that slices audio with silence detection

bspeech-mos-prediction icon bspeech-mos-prediction

A model for predicting MOS that utilizes embeddings of supervised learning and self-supervised learning models, combined with embeddings of speaker verification models, to predict the MOS metric.

capybara_dataset icon capybara_dataset

This is a dataset composed of images of capybaras to be used for training a model for object detection

capybara_object_detection icon capybara_object_detection

This repository presents how to train your own Object Detector Using TensorFlow Object Detection API. It also demonstrates how to use the trained model to annotate data (auto-annotate).

coqui-tts icon coqui-tts

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

cs7320-ai icon cs7320-ai

Examples for an AI course following the textbook Artificial Intelligence: A Modern Approach by Russell and Norvig.

data_augmentation_for_asr icon data_augmentation_for_asr

A set of audio augmentation techniques to perform noise insertion in datasets used for Automatic Speech Recognition.

deep-speaker icon deep-speaker

Deep Speaker: an End-to-End Neural Speaker Embedding System.

facebook_denoiser icon facebook_denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

freds0.github.io icon freds0.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

fullsubnet-plus icon fullsubnet-plus

The official PyTorch implementation of "FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement".

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