Explain How clustering is used in this project

[jeerank234]

How Clustering Groups Similar Songs (AudioMuse-AI)

[aishwary-vansh]

🎧 How Clustering Works in AudioMuse-AI

Clustering in AudioMuse-AI is a technique used to group songs based on their actual sound characteristics rather than relying on metadata like genre or artist. This allows the system to create more accurate and meaningful playlists.

🔍 Process Overview

1. Feature Extraction

Each song is analyzed using tools like Librosa to extract audio features such as:

• Tempo (speed)
• Pitch
• Energy
• Timbre (tone quality)

These features are converted into numerical data, forming a representation of the song.

2. Vector Embeddings

The extracted features are transformed into vector embeddings using models like CLAP.
In this high-dimensional space:

• Similar songs → closer together
• Different songs → farther apart

3. Similarity Measurement

The system calculates how similar songs are using:

• Cosine similarity
• Euclidean distance

This helps determine how closely related two songs are in terms of sound.

4. Clustering Algorithms

Algorithms like:

• K-Means
• Hierarchical clustering

are used to group songs into clusters. Each cluster contains songs with similar sonic characteristics.

🎶 Applications

Clustering enables:

• Automatic playlist generation
• Music discovery
• Visual music maps

Users can explore music based on how it sounds rather than predefined categories.

🧾 Summary

Clustering works by:

1. Extracting audio features
2. Converting songs into numerical vectors
3. Measuring similarity between songs
4. Grouping similar songs together

This results in smarter, more dynamic playlists and a better music exploration experience.

[NeptuneHub]

If you go here there is an entire chapter that exaplin how clustering is computed:

• https://github.com/NeptuneHub/AudioMuse-AI/blob/main/docs/ALGORITHM.md#2-song-clustering