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AI & Machine Learning Case Study
Music Genre Classification
Research Project: Acoustic feature engineering and neural network pipelines for automated music categorization.
87.5%accuracy
10 Classesgenres
< 1.2sinference
Project Overview
An automated audio intelligence system that scans digital music, extracts spectral signatures, and generates genre categories to organize music databases.
The Problem
Manual categorization of music collections is slow and highly subjective, creating bottlenecks for media publishers and streaming apps.
The Solution
Engineered audio wave processing pipelines using Librosa to compute Mel-Frequency Cepstral Coefficients (MFCCs), chroma matrices, and spectral centroids. Input variables into a deep CNN architecture.
System Architecture
Raw audio data (.wav) is decoded, split into segments, and converted into 2D MFCC spectrogram images. These spectrograms are fed into a 2D CNN model that classifies the audio.
[Acoustic WAV Audio] ==> [Librosa Feature Miner (MFCCs)] ==> [2D Spectrogram Array] ==> [CNN Model Classify]
Key Engineering Features
- ◆Automated audio spectrum visualizer and feature analyzer.
- ◆Multi-dimensional neural network classifying 10 music genres.
- ◆Clean visualization of spectral centroids and zero-crossing rates.
Technologies Utilized
PythonTensorFlowKerasLibrosaScikit-LearnMatplotlib
Repository StateOpen Source (MIT)
Deployment LevelStaging / Verified Node