RECOMMEND by Sound unleashes the full potential of a music catalog by offering personalized acoustic-based recommendations based on user search, interaction and behavior. Using intelligent audio technology, the module suggests music that sounds similar in terms of tempo, rhythmic feel, instrumentation and genre.

While traditional recommendation engines use collaborative filtering, RECOMMEND by Sound can generate reliable recommendations when:

  • meaningful social or sales statistics are unavailable
  • metadata is nonexistent or unreliable
  • metadata is inconsistent, for example when music catalogs from various labels are combined, each having its own unique annotation scheme


✔  New way of discovering music that matches taste
✔  Analyzes songs and recommends similar tracks based on acoustic patterns
✔  Playlist generation based on a song’s harmonic structure


Use Case:
Tom works for a company that develops innovative software solutions for music companies. A long-standing client asks Tom to propose a new generation search and recommendation engine that sets him apart from competitors.
Tom tries RECOMMEND by Sound, which is capable of offering personalized acoustic-based recommendations based on elements like mood, timbre, tempo and rhythmic feel without human curation. For the first time, Tom’s client can recommend similar sounding songs that are new, uncommon and unknown. Tom solves the long tail problem and his customer is satisfied.