About This Hackathon

Welcome to Week 13 of the Weekly MachineHack Hackathon Series!This week’s challenge is focused on developing a model that can distinguish between major and minor chords using audio data. Music is a language of patterns, and in this challenge, you’ll tap into the structure and rules of music theory to build a classifier for different chord types.<h3>About the Dataset</h3>The dataset contains audio files from two instruments: guitar and piano. In music, major chords are often associated with feelings of happiness and brightness, while minor chords evoke sadness or melancholy. Your task is to classify whether a tune is based on a major or minor chord using modern algorithms and machine learning techniques.<h3>Dataset Description:</h3><ul><li>Train.csv: Mapping for training audio files and their classification.</li><li>Test.csv: The dataset for which you will generate predictions.</li><li>Submission.csv: The format for submitting your predictions.</li></ul><h3>Participation and Benefits:</h3><ul><li>Skill Level: This challenge is designed for participants with experience in audio classification, signal processing, and machine learning.</li><li>Community Engagement: Join our Telegram group to connect with other participants, ask questions, and share insights.</li><li>Recognition: All participants will receive a MachineHack certificate, and top performers will be highlighted on the leaderboard.</li><li>Live Walkthrough: A live session will be held on 16th Oct 2024 to guide you through the challenge and provide expert tips.</li></ul><h3>Submission and Evaluation:</h3><ul><li>Submission Format: Submit your predictions in the provided Submission.csv file.</li><li>Evaluation Metric: Submissions will be evaluated based on the Accuracy Score.</li><li>Leaderboard: Track your performance and strive to reach the top!</li></ul><h3>How to Approach the Challenge:</h3><ol><li>Data Preprocessing: Extract key audio features (such as MFCCs, chroma, or spectrogram) to represent the audio signals effectively.</li><li>Feature Engineering: Focus on relevant signal processing techniques to handle the differences between major and minor chords.</li><li>Modeling Techniques: Experiment with models such as Logistic Regression, Random Forests, and deep learning architectures like CNNs or RNNs for audio classification.</li><li>Validation and Tuning: Use cross-validation and hyperparameter tuning to improve model performance.</li></ol>A starter notebook will be available to guide you through the initial data preprocessing steps and basic model building.<h3>Getting Started:</h3><ul><li>Register Now: Ensure you're registered to participate and receive updates.</li><li>Download the Dataset: Access the dataset from the MachineHack platform and start building your solution.</li><li>Join the Community: Connect with other participants via our Telegram group for tips, resources, and collaboration.</li></ul><h3>Support and Resources:</h3>For any questions, reach out to our support team at support@machinehack.com. Subscribe to our newsletter to stay informed of the latest updates.We’re excited to see your creative approaches!

Welcome to Week 13 of the Weekly MachineHack Hackathon Series!

This week’s challenge is focused on developing a model that can distinguish between major and minor chords using audio data. Music is a language of patterns, and in this challenge, you’ll tap into the structure and rules of music theory to build a classifier for different chord types.

About the Dataset

The dataset contains audio files from two instruments: guitar and piano. In music, major chords are often associated with feelings of happiness and brightness, while minor chords evoke sadness or melancholy. Your task is to classify whether a tune is based on a major or minor chord using modern algorithms and machine learning techniques.

Dataset Description:

Train.csv: Mapping for training audio files and their classification.
Test.csv: The dataset for which you will generate predictions.
Submission.csv: The format for submitting your predictions.

Participation and Benefits:

Skill Level: This challenge is designed for participants with experience in audio classification, signal processing, and machine learning.
Community Engagement: Join our Telegram group to connect with other participants, ask questions, and share insights.
Recognition: All participants will receive a MachineHack certificate, and top performers will be highlighted on the leaderboard.
Live Walkthrough: A live session will be held on 16th Oct 2024 to guide you through the challenge and provide expert tips.

Submission and Evaluation:

Submission Format: Submit your predictions in the provided Submission.csv file.
Evaluation Metric: Submissions will be evaluated based on the Accuracy Score.
Leaderboard: Track your performance and strive to reach the top!

How to Approach the Challenge:

Data Preprocessing: Extract key audio features (such as MFCCs, chroma, or spectrogram) to represent the audio signals effectively.
Feature Engineering: Focus on relevant signal processing techniques to handle the differences between major and minor chords.
Modeling Techniques: Experiment with models such as Logistic Regression, Random Forests, and deep learning architectures like CNNs or RNNs for audio classification.
Validation and Tuning: Use cross-validation and hyperparameter tuning to improve model performance.

A starter notebook will be available to guide you through the initial data preprocessing steps and basic model building.

Getting Started:

Register Now: Ensure you're registered to participate and receive updates.
Download the Dataset: Access the dataset from the MachineHack platform and start building your solution.
Join the Community: Connect with other participants via our Telegram group for tips, resources, and collaboration.

Support and Resources:

For any questions, reach out to our support team at support@machinehack.com. Subscribe to our newsletter to stay informed of the latest updates.

We’re excited to see your creative approaches!

AudioSamplesClassification