Musimap

Musimap operates as a provider of emotional artificial intelligence and metadata enrichment for the music industry. The company's technology has roots in a 1986 PhD project at the University of Louvain in Belgium, which involved a team of musicians, musicologists, and other experts mapping the global music library. Formally established as a company in 2015, Musimap was founded by individuals including Dr. Pierre Lebecque, a sociology and musicology researcher, and Frederic Notet, who serves as the chief technical officer. The foundational idea was to decode the "DNA of music" by linking every artist and track through relationships of influence and emotion.

The firm's business model is centered on providing its AI-powered services to other businesses through an API. Its clients include major record labels like Universal Music Group, Warner Music Group, and BMG, as well as streaming services, radio stations, and advertisers. Musimap generates revenue through tiered subscription packages for its API access, with pricing dependent on the volume of tracks analyzed and stored. The company's technology analyzes clients' music catalogs to extract detailed metadata, including genres, moods, and contextual situations. This enriched data allows for highly personalized music recommendations and the creation of targeted playlists. A significant milestone for the company occurred in October 2020 when it received a strategic investment from music icon Quincy Jones, who also became a special adviser. In September 2021, Musimap was acquired by the Swiss-based music-technology company Utopia Music, further integrating its technology into a broader infrastructure aimed at advancing creator rights and royalty payments.

Musimap offers a suite of products tailored to different industry needs. 'MusiMatch' is an audio-based similarity engine that uses machine learning to find acoustically similar tracks, moving beyond simple genre or tag-based searches. 'MusiMotion' analyzes audio to predict moods, genres, and suitable contexts for a song, assigning weighted scores to these attributes. Another key product is 'MusiMe', a psycho-emotional profiling engine that creates listener profiles detailing moods and values based on their listening history. This allows for a deeper understanding of consumer behavior for applications in marketing and advertising. The underlying technology leverages a neural network-like database containing billions of data points to process and connect a catalog of over 50 million songs.

Keywords: emotional AI, music metadata, music recommendation, B2B, API, music analysis, playlist generation, audio similarity, psycho-emotional profiling, music data enrichment, music technology, music curation, machine learning, deep learning, music discovery, creator rights, royalty payments, artist profiling, music industry, contextual music