.SUSS

Uplifting: Fresh, invigorating, and mood-boosting Energetic: Danceable, lively, and highly-active Relaxing: Kick back, chill out, and vibe Dark: Gritty, hard, unpredictable, and emotional Happy: Cheerful, bubbly, and carefree Tense: Edgy, angsty, with low or deep basslines Calm: Easy, smooth, and steady Melancholic: Moody, gloomy, and in your feelings More information about each track is also available such as voice presence, energy level, and energy dynamics: Voice Presence: the amount of singing voice throughout the full duration of the track, displayed as low, medium, or high Energy Level – the intensity of an analyzed track, displayed as low, medium, high, or variable Energy Dynamics – the progression of the Energy Level throughout the track, displayed as low, high, or variable

SWR Labs

1. AI Music Creation 2. Search & Recommendation 3. Auto-tagging 4. AI Mastering

Companies such as Cyanite, Musiio, Musimap, or FeedForward are able to extract descriptive metadata from the audio file.

Proprietary data is the data that remains in the hands of the company and rarely gets disclosed. Some data used by recommender systems is proprietary data. For example, a song’s similarity score is proprietary data that can be based on performance data. This type of data also includes insights from ad campaigns, sales, merch, ticketing, and etc.

Cyanite’s moods are presented in a comparison chart where overarching mood and the least present mood can be easily identified.

You can select up to 10 keywords from the Augmented Keywords set in the search bar. The chosen keywords will appear on the right with a weight bar and a cursor which you can place at any point between -1 to 1. For example, the keyword search input sparkling: 0.5, sad: -1, rock: 1, dreamy: 1, would refer to a search for a rock track that is dreamy, slightly sparkling, and not at all sad.

The Keyword Search by Weights:   Allows for increased relevance of search results  Provides better control over the outcome of the search Ensures a level of granularity and ability to create slight or total differentiation between tracks Takes away the stress of scrolling and checking through hundreds of search results Saves time on finding the right fitting songs for various use cases

Sonic Similarity Search

Similarity search algorithms usually work to compute the distance between songs in the space of different audio features. Calculating similarity distance allows to input a query that will then return tracks with the shortest similarity distances which means they are the most similar ones.

1. Finding similar songs using audio references for sync and music briefs

Using own songs and finding similar songs on Spotify to detect blind spots in a catalog

Finding samples across the catalog

n sample catalogs, Similarity Search algorithms can be used to find similar samples. Instead of spending hours digging through sample libraries, the user can select a reference sample or the first sample they like and get a list of similar-sounding samples.

Similarity Search can reduce search time up to 86% and offload some labor-intensive tasks from users as well as music catalog owners.

Two other companies that specialize in MIR (music information retrieval) are AIMS and Cyanite, who use AI music tagging – adding descriptive metadata identifiers to songs – and sonic similarity – search functionality that enhances the discoverability of material with matching audio characteristics – to extract value from catalogs. The use case scenarios here are multiple. Music rights holders can deploy the technology to tag vast collections, maximizing their assets and monetizing not just the hits but every song. It also promises to be an incredibly powerful tool for sync and music licensing, particularly during a time when demand is exploding across the entertainment sector and beyond.

AI Music Creation / Search & Recommendation / Auto-tagging / AI Mastering

BPM / Key / Mood / Main Genre / Sub Genre / Voice / Emotional Profile / Instruments / Energy Level / Musical Era

The results they produced feel detailed, accurate and informative, bringing tangible sonic representation from the language of machine code.

Text Search will provide a list of suitable tracks. The search requests may be as complex as “A city lost to time, its buildings half buried by the jungle that has claimed it” or as simple as “Walking down a dusty highway”.

Cyanite helps music companies turn their catalogs into their own personal Spotify – providing music libraries with the usability, transparency and functionality users have become accustomed to.

But unlike LLMs, the music industry has copyright law to enforce the temporary halt of new training models (at least in those areas where it is enforceable). So what if the UMG-attempted halt of new generative AI training allows us to take a step back and try to get an objective perspective on recent developments? This is something that is not possible with LLMs, because training data is so much more accessible and less controllable. Which is the reason people have to write open letters in the first place – a strategy which has somewhat questionable expectations of success.

To realize the power of artificial intelligence to extract value from music catalogues, you need to understand how AI-driven curation works.