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.
![](https://github.com/DrStef/Deep-Learning-and-Digital-Signal-Processing-for-Environmental-Sound-Classification/raw/main/esc10_v23_100pc.png)
![](https://github.com/DrStef/Deep-Learning-and-Digital-Signal-Processing-for-Environmental-Sound-Classification/raw/main/MelSpectrogram.png)
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"
Multi-feature Convolutional Neural Networks (CNN) achieves accuracy close to 99%, with custom pre-processing and a fusion mel-spectrograms + complex wavelet transforms.
The last confusion "sea waves" "rain" is solved by developping an original transform of the complex CWT. This Transform, aT-CWT replaces the phase of the CWT for stationary, pseudo-stationary sounds with a Gaussian characteristics.
With the aT-CWT transform, the multi-feature CNN model achieves 100% accuracy.
![](https://github.com/DrStef/Machine-Learning-and-Digital-Signal-Processing-for-Genome-Classification/raw/main/LOG_REG_Fungi_ConfusionMatrix.png)
"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.
![](https://github.com/DrStef/Applied_Data_Science_Capstone_SpaceX_IBM/raw/main/spacex-MEW1f-yu2KI-unsplash.jpg)
![](https://github.com/DrStef/Machine_Learning_with_Python-IBM/raw/main/density_based_clustering_weather_001.png)
![](https://github.com/DrStef/House_Sales_in_King_County_USA_IBM/raw/main/Project_House_Sales_in_King_County_USA_v003_LTX/output_60_0.png)
One Jupyter Notebook includes interactive Folium maps (interactive maps will not display on Github).
![](https://github.com/DrStef/Statistics-for-Data-Science-with-Python/raw/main/real_estate_boston_001.png)
🔭 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