This absolutely rocks: like an open and usable version of the once beloved and now deprecated Echo Nest Audio Analysis api [0]. There used to be a tool to use this data to manage your own (Spotify-only) music library and while it wasn’t magic, it sure felt like it.
If you're like me, you have a self-hosted music library you've spent years curating. It’s running on a server like Jellyfin, Navidrome, or LMS, and it’s massive. And let's be honest: you probably listen to the same 10% of it.
We rely on text searches, artist lists, and rigid genre tags that often fail to capture the feel of a song. What if you could see your entire music library, not as a list of files, but as a galaxy of sound?
Introducing the new *Music Map* feature in [AudioMuse-AI](https://github.com/NeptuneHub/AudioMuse-AI), the open-source sonic analysis tool for your personal music library. It’s not just a new feature; it’s a new way to explore your collection.
### *What is the Music Map?*
The Music Map, introduced in version 0.7.2-beta, is a vibrant, interactive 2D visualization of your entire music library.
Here’s how it works:
1. *Sonic Analysis:* AudioMuse-AI listens to every song in your library and generates a complex "sonic fingerprint" (known as an embedding) that describes its acoustic properties—beyond what any genre tag ever could.
2. *2D Projection:* It then uses powerful machine learning techniques (like UMAP or PCA) to take that complex data and plot every single song as a dot on a 2D map.
The result? *Songs that sound similar are placed close together.*
You end up with a stunning visual representation of your music. You’ll see "islands" of calm acoustic tracks, dense "continents" of high-energy electronic music, and winding "rivers" of classical pieces, all clustered organically by their actual sound. The map even color-codes songs by their dominant mood, so you can spot the "happy" or "melancholy" regions at a glance.
### *Beyond a Pretty Picture: A Launchpad for Discovery*
This is where it gets really exciting. The map isn't just for looking; it's for interacting. You can pan, zoom, and hover over any dot to see what song it is. It’s an incredible tool for rediscovering tracks you forgot you even had.
But its real power is unlocked when you combine it with AudioMuse-AI's other features.
*1\. Visually Discover Your Start and End Points*
Ever wondered what the musical journey would sound like from a high-energy punk track to a quiet ambient piece?
With the Music Map, you can. Visually locate those two songs on the map—one in the "high-energy" cluster and one in the "calm" cluster.
*2\. Create a "Song Path"*
Once you’ve identified your two songs (A and B), you can feed them into AudioMuse-AI’s *Song Path* feature.
This isn't a random shuffle. As detailed in the path\_manager.py logic, the system intelligently generates a seamless path of songs from your library, bridging the sonic gap step-by-step. It’s like a musical GPS finding the most logical route between two auditory destinations.
*3\. Instantly Create a Playlist*
You’ve explored the map, found two perfect songs, and generated a seamless path between them. Now what?
With a single click, AudioMuse-AI's voyager\_manager.py takes that list of song IDs and instantly creates a brand new playlist on your media server. Whether you use Jellyfin, Navidrome, LMS, or Emby, your new "Song Path" is saved and ready to play.
### *How to Get Started*
Getting started is simple. If you're running AudioMuse-AI (version 0.7.2-beta or newer), just make sure you've run the initial sonic analysis of your library. The backend logic from app\_map.py will automatically build the cache for your map, and it will be available to explore.
### *Your Music Library is No Longer a List. It’s a Universe.*
The new Music Map feature fundamentally changes how you interact with your personal music collection. It moves us beyond simple text searches and into the realm of true visual, sonic exploration.
Stop scrolling through lists. It’s time to explore your music.
This absolutely rocks: like an open and usable version of the once beloved and now deprecated Echo Nest Audio Analysis api [0]. There used to be a tool to use this data to manage your own (Spotify-only) music library and while it wasn’t magic, it sure felt like it.
Super excited to try AudioMuse
[0]: https://developer.spotify.com/documentation/web-api/referenc...
If you're like me, you have a self-hosted music library you've spent years curating. It’s running on a server like Jellyfin, Navidrome, or LMS, and it’s massive. And let's be honest: you probably listen to the same 10% of it.
We rely on text searches, artist lists, and rigid genre tags that often fail to capture the feel of a song. What if you could see your entire music library, not as a list of files, but as a galaxy of sound?
Introducing the new *Music Map* feature in [AudioMuse-AI](https://github.com/NeptuneHub/AudioMuse-AI), the open-source sonic analysis tool for your personal music library. It’s not just a new feature; it’s a new way to explore your collection.
### *What is the Music Map?*
The Music Map, introduced in version 0.7.2-beta, is a vibrant, interactive 2D visualization of your entire music library.
Here’s how it works:
1. *Sonic Analysis:* AudioMuse-AI listens to every song in your library and generates a complex "sonic fingerprint" (known as an embedding) that describes its acoustic properties—beyond what any genre tag ever could. 2. *2D Projection:* It then uses powerful machine learning techniques (like UMAP or PCA) to take that complex data and plot every single song as a dot on a 2D map.
The result? *Songs that sound similar are placed close together.*
You end up with a stunning visual representation of your music. You’ll see "islands" of calm acoustic tracks, dense "continents" of high-energy electronic music, and winding "rivers" of classical pieces, all clustered organically by their actual sound. The map even color-codes songs by their dominant mood, so you can spot the "happy" or "melancholy" regions at a glance.
### *Beyond a Pretty Picture: A Launchpad for Discovery*
This is where it gets really exciting. The map isn't just for looking; it's for interacting. You can pan, zoom, and hover over any dot to see what song it is. It’s an incredible tool for rediscovering tracks you forgot you even had.
But its real power is unlocked when you combine it with AudioMuse-AI's other features.
*1\. Visually Discover Your Start and End Points*
Ever wondered what the musical journey would sound like from a high-energy punk track to a quiet ambient piece?
With the Music Map, you can. Visually locate those two songs on the map—one in the "high-energy" cluster and one in the "calm" cluster.
*2\. Create a "Song Path"*
Once you’ve identified your two songs (A and B), you can feed them into AudioMuse-AI’s *Song Path* feature.
This isn't a random shuffle. As detailed in the path\_manager.py logic, the system intelligently generates a seamless path of songs from your library, bridging the sonic gap step-by-step. It’s like a musical GPS finding the most logical route between two auditory destinations.
*3\. Instantly Create a Playlist*
You’ve explored the map, found two perfect songs, and generated a seamless path between them. Now what?
With a single click, AudioMuse-AI's voyager\_manager.py takes that list of song IDs and instantly creates a brand new playlist on your media server. Whether you use Jellyfin, Navidrome, LMS, or Emby, your new "Song Path" is saved and ready to play.
### *How to Get Started*
Getting started is simple. If you're running AudioMuse-AI (version 0.7.2-beta or newer), just make sure you've run the initial sonic analysis of your library. The backend logic from app\_map.py will automatically build the cache for your map, and it will be available to explore.
### *Your Music Library is No Longer a List. It’s a Universe.*
The new Music Map feature fundamentally changes how you interact with your personal music collection. It moves us beyond simple text searches and into the realm of true visual, sonic exploration.
Stop scrolling through lists. It’s time to explore your music.
Find out more and get started with AudioMuse-AI on [GitHub](https://github.com/NeptuneHub/AudioMuse-AI).