This section contains Research and Development projects in Machine Learning and Deep Learning that require original developments. They call on our expertise in Digital Signal Processing, Optimization, Calculus, Linear Algebra.
Automatic environmental sound classification (ESC) based on ESC-50 dataset (and ESC-10 subset) built by Karol Piczak and described in the following article: "ESC: Dataset for Environmental Sound Classification." by Karol J. Piczak. 2015. In Proceedings of the 23rd ACM international conference on Multimedia (MM '15). Association for Computing Machinery, New York, NY, USA, 1015–1018. https://doi.org/10.1145/2733373.2806390"
Convolutional Neural Networks (CNN) achieves accuracy close to 99%, with custom pre-processing and a fusion mel-spectrograms + complex wavelet transforms.
In this project we develop effective methods for classifying mitochondrial genomes (DNA sequences) based on Digital Signal Processing, Machine Learning, Deep Learning. This is on-going research and results will be published on a regular basis. As a starting point we analyzed the following paper: "ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels" by Gurjit S. Randhawa , Kathleen A. Hill and Lila Kari. https://doi.org/10.1186/s12864-019-5571-y
Their alignement free DNA sequence classification approach: ML-DSP is very effective. By introducing a simple alignment technique and short FFTs: ML-FFT + SoftAlign, we outperform ML-DSP with difficult datasets: Fungi, Insects.
This section is a portfolio of Machine Learning projects with Python and various visualization and analysis tools. Most of these projects were carried out within the framework of IBM certifications. They are presented with Jupyter Notebooks.
Some projects have been improved by incorporating more in-depth data analysis, better graphs, advanced ML techniques.
In this project, we predict if the Falcon 9 first stage will land successfully. Project includes: SpaceX data collection, Data Wrangling, Webscraping, EDA with SQL Queries & Data visualization, SpaceX Launch Records Dashboard, Launch Sites Locations Analysis with Folium, Machine Learning classification with optimization of hyperparameters and selection of best model: KNN, Decision Tree, SVM, Logistic Regression. 
A widerange of small projects with various ML techniques, prediction, supervised and unsupervised classification: Linear Regression, Polynomial Regression, Non-Linear Regression, Recommandation Systems, KNN, Customer Segmentation with K-Means, Hierarchical Clustering, Density-Based Clustering, Logistic Regression. 
The project consists of finding the best model for predicting home prices in King County, USA in Washington State, based on a dataset of homes sold between May 2014 and May 2015. Prediction accuracy was improved by implementing a spline regression model.One Jupyter Notebook includes interactive Folium maps (interactive maps will not display on Github).
Loan Status Prediction using Supervised Classification Algorithms: KNN, Decision Tree, SVM, Logistic Regression.
Old dataset on housing prices derived from the U.S. Census Service to present insights based on our experience in Statistics. Median value of houses bounded by the Charles river, of owner-occupied units built before 1940, relationship between Nitric oxide concentrations and the proportion of non-retail business acres per town, impact of weighted distance to the five Boston employment centres on the median value of owner-occupied homes. 
Dataset: car dataset including various makes, specifications and prices.
After cleaning the dataset, running statistics, identifying the most relevant variables, we develop several models that will predict the price of a car using a set of features/variables.
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Word Cloud |
Folium with markers |
Choropleth |
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"Figure 8" toroid |
Gyroid |
Truncated cuboctahedron |
Helicoid-Catenoid |
🔭 I’m currently working on advanced projects in ML & DL
👯 I’m looking to collaborate on Digital Signal Processing, Machine Learning, Deep Learning
📫 How to reach me: stephane.dedieu@bloo-audio.com