idrissi1998 Goto Github PK

followers: 2.0 following: 23.0 repos: 27.0 gists: 0.0

Type: User

Company: Université de Sherbrooke

Location: Sherbrooke, QC, Canada

Blog: http://idrissi1998.github.io/

Hi 👋, I'm Ismail IDRISSI

Passionate about data science and web development.

👨🏻‍💻 Studies background

Specialized graduate diploma in computer science from the Faculty of Sciences of the University of Sherbrooke - Canada

Specialized graduate diploma in engineering from the Faculty of Engineering of the University of Sherbrooke - Canada

Computer science engineer from Mohammadia School of engineers (EMI) - Morocco

Tools and langages

🤝🏻 Connect with Me

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idrissi1998's Projects

adn_turtle_schema

Réalisation d'un schéma à partir d'une séquence ADN

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

catalog-of-moroccan-schools-and-universities

classification_of_tree_leaves

Analysis of learning models for the classification of tree leaves

coming-soon

Beautiful Animated Bootstrap Coming Soon Template

doorstep

The powerful e-commerce solution for Python

free-certifications

A curated list of free courses & certifications.

gin811_speech_enhancement_with_snn

Speech enhancement with spiking neural networks (Using NengoDL, Keras and Tensorflow).

imminent

Free 3D Parallax Responsive Coming Soon Template

music-separation-with-neural-networks-demucs-architecture

Code for the paper Music Source Separation in the Waveform Domain

speech-enhancement-with-neural-networks-demucs-architecture

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.