Academic & AI researchAI & ML

Music Genre Classification

Acoustic feature engineering and neural network pipelines for automated music categorization.

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Headline results

Genres: 10 Classes
Accuracy: 87.5%
Inference: < 1.2s

The project in 6 images

5 summary slides · 1 genuine image from the project

Music Genre Classification: Music Genre Classification. Acoustic feature engineering and neural network pipelines for automated music categorization.

Slide 01 of 06: Cover

Cover

Acoustic feature engineering and neural network pipelines for automated music categorization.

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01Overview

The project at a glance

An automated audio intelligence system that scans digital music, extracts spectral signatures, and generates genre categories to organize music databases.

Type: Academic & AI research
Category: AI & ML

Tech stack

Python
TensorFlow
Keras
Librosa
Scikit-Learn
Matplotlib

02The challenge

The research question

Manual categorization of music collections is slow and highly subjective, creating bottlenecks for media publishers and streaming apps.

The brief

Acoustic waves are highly unstructured. Extracting clear features that distinguish similar musical styles (e.g. Jazz vs. Blues) is difficult.

03The solution

The model pipeline

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.

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.

01
Data
- Librosa
02
Model
- TensorFlow
- Keras
- Scikit-Learn
03
Evaluation
- Genres 10 Classes
- Accuracy 87.5%
04
Output
- AI & ML
- Inference < 1.2s

Model pipeline, from data to output

Data-flow sketch

[Acoustic WAV Audio] ==> [Librosa Feature Miner (MFCCs)] ==> [2D Spectrogram Array] ==> [CNN Model Classify]

04Key features

What it does

01Automated audio spectrum visualizer and feature analyzer.
02Multi-dimensional neural network classifying 10 music genres.
03Clean visualization of spectral centroids and zero-crossing rates.

05Results

What it achieved

Reached 87.5% classification accuracy across 10 distinct genres, outperforming traditional machine learning classifiers by 18%.

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Music Genre Classification

Acoustic feature engineering and neural network pipelines for automated music categorization.