Finding great new music can be a very rewarding experience, but sometimes the effort that goes into music discovery often frustrates even the most determined of us.
Thanks to the Internet, we now have access to millions of songs but paradoxically more becomes less. It’s getting harder to discover new music, especially when 150 000 new tracks are released into the worldwide catalogue each month.
This is where niland™ comes into the picture and offers new tools and solutions to to build musically relevant experiences.
niland™’s services rely on the combination of our expertise in machine learning, audio signal processing and algorithm. Our recommendations and search systems are based on a music-centric approach that goes beyond any statistical approach. We invented the music2vec, in order to map and compare the characteristics of any kind of music and modelize the taste of listeners. Our algorithm examines thousands of data points to understand the intrinsic qualities of the music (genre, rhythm, type of voices, decade, mood, instrumentation…) and draw the musical DNA picture of a song, just like a human ear would do to say “yeah, it sounds like”.
Our algo-based solutions have been validated by human perception tests and assessed as relevant as human-based recommendation systems. Our technology was developed by the co-founders of niland™, while they were researchers in one of best European institutes about music and science. This technology was elected the best of the academic world for the last two years at the MIREX competition.
Thanks to our unique technologies and our deep knowledge of the market, we develop B2B solutions (for music publishers/editor/distributor, music streaming services and app makers) that can be used on mobile devices, desktops, servers and the web. Our mission is to make people find their next favorite song.
niland™ was founded by music-lover entrepreneurs in 2013 and is based in Paris.
And as we like to say at niland™, no song is an island.