Mun'im Zabidi's Projects
Proposed a system which classifies animal sound using a deep convolutional neural network. This repo contains animal sounds used in this work.
A collection of ML scripts to test the M1 Pro MacBook Pro
The [ARM] Assembler language definition for the latex listings package
Multi class audio classification using Deep Learning (MLP, CNN): The objective of this project is to build a multi class classifier to identify sound of a bee, cricket or noise.
A series of Jupyter notebooks and python files which stream audio from a microphone using pyaudio, then processes it.
Scripts to convert audio files to spectrograms and back
:sunglasses: Curated list of awesome lists
A curated list of awesome Deep Learning tutorials, projects and communities.
A curated list of awesome Machine Learning frameworks, libraries and software.
:book: A curated list of resources dedicated to Natural Language Processing (NLP)
TensorFlow - A curated list of dedicated resources http://tensorflow.org
Keras implementations of BinaryNet and XNORNet
Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.
Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.
Code for searching the www.xeno-canto.org bird sound database, and training a machine learning model to classify birds according to their sounds.
A predictive model to identify four species of bird from their vocalisations. It is my contribution to a Hackster competition.
Use image recognition models to classify sounds of 20 different species of birds in the Aiguamolls de l'Empordà natural park in Catalonia, Spain.
BirdNET analyzer for scientific audio data processing.
Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.
Bird sound classification by different CNNs and preprocessing methods
Source codes for the manuscript “Automatic recognition of element classes and boundaries in the birdsong with variable sequences” by Takuya Koumura and Kazuo Okanoya.
A pre-trained deep learning system for detecting bird flight calls in continuous recordings
Code for black audio book
Meta-data and Makefile needed to build the book. Main starting point.
Thomas Grill's "bulbul" bird audio detection system, adapted for DCASE 2018
vgg cifar-10
INT-Q Extension of the CMSIS-NN library for ARM Cortex-M target
Computer Networking : Principles, Protocols and Practice (first and second edition, third edition is being written on https://github.com/cnp3/ebook)