Project Organization

Inspired by: https://mengdong.github.io/2018/05/28/Python-Machine-Learning-Project-Template/

CRISP-DM Process Model

https://web.archive.org/web/20220401041957/https://www.the-modeling-agency.com/crisp-dm.pdf

Business Understanding

Objectives

Create an automatic music genre recognition (MGR) system and web portal for it.

1. Build a dataset containg song metadata and their various genres and spectrograph info.
2. Develop a pipeline to import audio clips from datasets
3. Create a web app front-end (can run on desktop).
4. Host the program as a web server.
5. Develop a program to run a user-submitted audio clip against the model and print results witha ccuracy metrics
6. Content based recommender system for music similar to audio clip
   • Train a neural network

Situation

The user will enter a song clip, then receive a formatted top-n list of genres sorted by confidence value in descending order.

Data Mining Goals

Technologies, Libraries, Tools

• Poetry: Project Dependency Management Tool
• Pytorch: Machine Learning
• Librosa: Audio and Music Proccessing
• Matplotlib: Data Visualization
• Numpy: General purpose array-processing
• Pandas: Data analysis and manipulation tool
• Morgan: logger
• Pytest: Test framework

Data Understanding

Datasets

• GTZAN Genre Collection by G. Tzanetakis and P. Cook
• Million Song Dataset by LabROSA and The Echonest

Data Description

Exploratory Data Analysis

Data Quality Analysis

Data Preparation

Select Data

Clean Data

Construct Data

Integrate Data

Format Data

Modeling

Select Modeling Techniques

Generate Test Design

Build Model

Assess Model

Evaluation

Evaluate Results

Review Process

Next Steps

Deployment

Plan Deployment

Plan Monitoring and Maintenance

Produce Final Report

Review Project