Overview
SONOTELLER is an AI-powered music analysis engine designed to extract structured information from songs and lyrics. Its analysis can cover genres and subgenres, moods, instruments, BPM, musical key, vocal characteristics, language, lyrical themes, and explicit-content indicators.
The supplied testing record marks SONOTELLER as personally tested, but the transcript is empty and the only observation provided is “NOTES.” I therefore cannot truthfully describe a specific song I entered, YouTube URL I used, analysis I generated, or result I received during the test.
According to available product information, the web demo can analyze a song using an artist/title search or YouTube URL, while the API is intended for analyzing and tagging users’ own music files and larger catalogs. The service also identifies a song’s “Golden Minute,” referring to a highlighted section such as a chorus or other key moment.
That makes SONOTELLER more relevant to music metadata and discovery workflows than to music generation. It can be useful for catalog organization, playlist research, sync-licensing workflows, and automated tagging. Because the supplied transcript contains no actual interaction, analysis accuracy and processing speed were not independently assessed in this review.
Pricing & Plans
The public SONOTELLER web demo is reported as free for song analysis through YouTube URLs, while API access is available for automated analysis of music catalogs. Current API pricing is not consistently presented across available sources, so I would check pricing before publishing a definitive API price. Some current third-party listings report API tiers such as a free Basic allowance and paid Pro, Ultra, and Mega plans, but these figures could not be verified directly against an accessible official pricing page.
Key Features
- Song Analysis
Analyzes musical characteristics including genre, mood, instruments, BPM, and key.
- Lyrics Analysis
Summarizes lyrics and identifies language, themes, mood, and explicit-content indicators.
- Golden Minute
Identifies a highlighted or particularly relevant section of a song.
- Automatic Tagging
Produces structured metadata useful for organizing and categorizing music catalogs.
- API Access
Supports automated analysis and tagging for larger music collections and integration workflows.
Popular Use Cases
- Music Catalog Management
Enrich large libraries with consistent musical and lyrical metadata.
- Playlist Curation
Identify genres, moods, instruments, and other attributes when evaluating tracks.
- Sync Licensing
Search and organize music according to characteristics relevant to film, advertising, or other media.
- Music Discovery
Add descriptive metadata that can improve filtering and discovery across catalogs.
- Automated Tagging
Process larger collections programmatically through the API.
Best For
- Music labels and publishers
- Music supervisors
- Playlist curators
- Music catalog managers
- Developers building music metadata workflows
What to Verify Before Choosing
- Current API pricing and request allowances.
- Whether your required audio-file workflow needs API access.
- Current API documentation and integration limits.
- Accuracy for your particular genres and catalog.
- Current data-retention and privacy terms for uploaded music.
Frequently Asked Questions
What is SONOTELLER?
SONOTELLER is an AI music analysis engine that extracts musical and lyrical information from songs for discovery, cataloging, and tagging.
Is SONOTELLER free?
The web demo is reported as free for YouTube-based song analysis, while API access and professional usage may require paid plans.
How much does SONOTELLER cost?
Current API pricing is not reliably verified from an official pricing page; check the provider before purchasing API access.
What did I test?
The supplied record marks SONOTELLER as personally tested, but the transcript contains no documented workflow or result, so specific hands-on testing cannot be verified.
Who is SONOTELLER best for?
It is best suited to music professionals and developers who need automated song analysis, metadata enrichment, catalog organization, or music discovery workflows.