What is it?

Musimap leverages 20 years of human research, Audio-processing and AI. Musimap applies the latest machine learning and deep learning technologies to music, nourished by a database of 1 million tracks that were manually labelled by music experts. We perform cloud-based big data analyses from the global catalog of music to extract information about genres, sub-styles, vocalness, complex moods, contextual situations and dozens of other parameters. Musimap’s APIs scan this proprietary database in a fraction of a second and match the data with any client’s catalogue to allow unprecedented accuracy in music recommendation and the creation of advanced music applications.

Musimap’s technology evolved into a self-learning, context-aware neuronal music network powered by emotion AI. Owning a rapidly growing and uniquely advanced music database exceeding 3 billion data points, 2B relations, 35+ million tracks from 2.5 million artists, 1300 keywords, 70 genres, 70 complex moods, and 70 situations, the company is quickly establishing itself as the leading emotion-sensitive music recommendation technology.

Why Musimap’s technologies are unique and disruptive

  • Artificial Emotional Intelligence
  • Latest machine learning and deep learning technologies
  • One of the largest manually tagged music catalogues : 1M tracks
  • Fully automated
  • Robust and scalable architecture
  • Easy access through our set of API’s
  • Strong metadata accuracy including moods

Musimap’s Origin Story

Interdisciplinary human expertize is the backbone of Musimap’s technology. Finding its roots in a PhD on musical morphing from the University of Louvain (Louvain-la-Neuve, Belgium) back in 1986, Musimap’s technology has been developed by 60 renowned musicologists, sociologists, psychologists, and philosophers. Key music experts and artists also contributed to refining the technology. Together, they mapped the global music library as a detailed map of the history of music, that soon evolved into a self-learning, context-aware neuronal music network. Over the past years, the algorithm has been continuously improved to meet current and future technological standard and application requirements.

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