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NeptuneHub
AudioMuse-AI
Python2025–2025
NeptuneHub / AudioMuse-AI

AudioMuse-AI

AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.

2Distinct people
4Mentions
0Explicit recommenders
2.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week4 dated mentions

Observed mentions: 2025-09-13 07:16:50 UTC to 2025-12-15 07:48:12 UTC. Weeks start Monday, UTC.

0122025-09-08 00:00:00 UTC to 2025-09-15 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsSep 82025-09-15 00:00:00 UTC to 2025-09-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-22 00:00:00 UTC to 2025-09-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-29 00:00:00 UTC to 2025-10-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-06 00:00:00 UTC to 2025-10-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-13 00:00:00 UTC to 2025-10-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-20 00:00:00 UTC to 2025-10-27 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-10-27 00:00:00 UTC to 2025-11-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 272025-11-03 00:00:00 UTC to 2025-11-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-10 00:00:00 UTC to 2025-11-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-17 00:00:00 UTC to 2025-11-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-24 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2025-12-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-08 00:00:00 UTC to 2025-12-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-15 00:00:00 UTC to 2025-12-22 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsDec 15

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 82025-09-08 00:00:00 UTC2025-09-15 00:00:00 UTC110
Sep 152025-09-15 00:00:00 UTC2025-09-22 00:00:00 UTC000
Sep 222025-09-22 00:00:00 UTC2025-09-29 00:00:00 UTC000
Sep 292025-09-29 00:00:00 UTC2025-10-06 00:00:00 UTC000
Oct 62025-10-06 00:00:00 UTC2025-10-13 00:00:00 UTC000
Oct 132025-10-13 00:00:00 UTC2025-10-20 00:00:00 UTC000
Oct 202025-10-20 00:00:00 UTC2025-10-27 00:00:00 UTC211
Oct 272025-10-27 00:00:00 UTC2025-11-03 00:00:00 UTC000
Nov 32025-11-03 00:00:00 UTC2025-11-10 00:00:00 UTC000
Nov 102025-11-10 00:00:00 UTC2025-11-17 00:00:00 UTC000
Nov 172025-11-17 00:00:00 UTC2025-11-24 00:00:00 UTC000
Nov 242025-11-24 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 12025-12-01 00:00:00 UTC2025-12-08 00:00:00 UTC000
Dec 82025-12-08 00:00:00 UTC2025-12-15 00:00:00 UTC000
Dec 152025-12-15 00:00:00 UTC2025-12-22 00:00:00 UTC110

What Hacker News says

4 sources
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
Read the rest
. *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).
In Stop Scrolling, Start Exploring: AudioMuse-AI's New Music Map Changes Everything

Show HN: AudioMuse-AI Sonic Analysis

I just want to share my free and opensource GitHub repo, AudioMuse-AI: https://github.com/NeptuneHub/AudioMuse-AI It is able to do sonic analysis locally on your homelab thanks to Librosa and Tensorflow and the thanks of this analysis can find sonic similar song, useful for instant mix, playlist creation or song path. Thanks to API it works with Jellyfin and all the open subsonic API music server like Navidrome or Lightweight Music Server (LMS). If you like please share your feedback on GitHub and leave a star on the repository.
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