Sound-Classification-using-Quanvolutional-Neural-Networks

[Abou-el-ela]

Team Name: Two Bits in a Box

Project Description: Sound classification is one of the popular topics in the classical machine learning literature eg.[1],[2]. One of the used methods is applying CNN to the spectrograms of the sound samples. Nevertheless, we couldn't find similar applications in the Quantum Machine Learning literature.

In this project we aim to use Quanvolutional Neural Networks to classify sound using this kaggle dataset. We will mainly compare the performance of the Quanvolutional Neural Networks to the equivalent classical CNN implementation, and explore techniques in the Quantum Machine Learning literature that can enhance the existing classical ML techniques.

Source code: https://github.com/heba0/Sound-Classification-using-Quanvolutional-Neural-Networks

Resource Estimate: The AWS credit will help us experiment better with Quantum Computing resources. Our model will use around 3 layers with 3x3 kernels -> 3x3x3 = 27 qubits per task We would like to use the Rigetti with 2000 shots The training and testing datasets have around 9700 samples (can be sampled to smaller datasets)

References: [1] Jaiswal, K. and Kalpeshbhai Patel, D., 2018. Sound Classification Using Convolutional Neural Networks. 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM),.

[2] Davis, N. and Suresh, K., 2018. Environmental Sound Classification Using Deep Convolutional Neural Networks and Data Augmentation. 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS),.

Your team's name (matching the name used on the QML Challenge Scoreboard)

Presentation:

1. https://github.com/heba0/Sound-Classification-using-Quanvolutional-Neural-Networks/blob/master/Dataset%20Info%20%26%20Preparation.ipynb

2. https://github.com/heba0/Sound-Classification-using-Quanvolutional-Neural-Networks/blob/master/Preprocessing.ipynb

3. https://github.com/heba0/Sound-Classification-using-Quanvolutional-Neural-Networks/blob/master/Preprocessing.ipynb

[co9olguy]

Thanks for the submission! We hope you have enjoyed participating in QHack :smiley:

We will be assessing the entries and contacting the winners separately. Winners will be publicly announced sometime in the next month.

We will also be freezing the GitHub repo as we sort through the submitted projects, so you will not be able to update this submission.