• MUSICOVERY

    Big data for innovative music experiences

  • WHAT WE DO

     

    Musicovery is a high quality and comprehensive music recommendation engine, very easy to integrate through its API.

    It provides 4 types of services:

    • descriptive metadata on artists and tracks (genres, moods, era, geographic, acoustics descriptors…)
    • recommendations and playlists, personalized in real time
    • bespoke webservices to provide specific content (recommendation of live concerts, Youtube channels,…)
    • advice on data analysis, algorithms, recommendation optimization, metadata sourcing and music UX design
     

    With more than 10 years of experiments on how to provide intuitive, rich and smart radios, how to make sense of behavioural data and produce precise descriptive metadata on music content, Musicovery is in a unique position to provide a comprehensive recommendation engine. It generates any kind of recommendations and playlists: from a mood, a song, an artist, a subgenre, a theme, a place, or for a specific listener.

     

    Musicovery measures the quality of recommendations and playlists with an analytic tool that optimizes recommendations and playlists to each listener.

     

    Recommendations and playlist are provided through an API, very easy to integrate, especially for prototyping new innovative UX.

     

  • METADATAS

    Musicovery provides metadata on:

    • tracks :
      • moods: key words like "happy", and mood quantitative value (valence/arousal)
      • activities : listening situations like music for driving, working, partying…
      • genres
      • acoustic descriptors
    • artists : genres, era, role, geographic location 

    Metadata are attributed by experts (for the top of the catalog and archetypical songs of each genres/style/moods) and automatically by machine learning from audio and semantic web (for the long tail of catalogs).

     

    Musicovery indexes systematically the commercial catalog and beyond you can upload your audio files to Musicovery API to get the corresponding metadata.

     

    Moods and activities indexation are the result of extensive researchs conducted on psychological states mapping (circumplex models like Russell, Plutchik,...), their mapping with acoustic descriptors, and on automatic indexation by machine learning (R&D projects with Ircam on music information retrieval).

  • RECOMMENDATION ENGINE

    Musicovery API generates the best playlists and recommendations from a mood (calm, happy,...), an artist, a track, a genre/style, a context/activity (for driving, working, partying,...), a theme, a period/year, a location (city, region, country, continent).

     

    Recommendations of tracks, artists, genres and playlists are personalized in real time to each listener, according to his music preferences, listening behaviour and listening history.

     

    With very few information on a listener music preferences and listening behavior, Musicovery engine starts very early to personalize recommendations and playlists with a high degree of relevance.

     

    Try a playlist:

    Playlists and recommendations can be restricted to a specific catalogue, and be optimized for a specific UX and a specific audience

  •  API

     

    Musicovery API makes it very easy to provide descriptive metadata, recommendations and playlists in real time.

     

    You can test the API freely and look at the results returned by the API for the following example:

    Ex. : get a playlist from Skrillex, with artists little known from the same genre (dubstep)

     

    Musicovery API provides mapping between the identifiers of the major players of the industry. Clients can for instance use Musicovery API with the Facebook id of an artist as input and get as output Deezer ids of similar artists.

     

    To learn about all the fonctionalities provided by Musicovery, please read the documentation.

  • WEBSERVICES & ADVICE

    Musicovery provides also services like personalized and geolocalised recommendation of live concerts, personalized Youtube channels, emerging artists for specific regions and genres…

     

    These services require to identify relevant sources of data and content partners and to map identifiers of the content partners.

    Musicovery sets up bespoke webservices tailored to the specific needs of its clients.

     

    To make the most of music recommendation services, Musicovery provides its clients with advice on:

    • Recommendation optimization (data analysis, algorithm design, recommendation quality measurement, A/Z tests, algorithm benchmark)
    • Music content and metadata sourcing
    • Music UX design and dataviz.

  • News

    Musicovery, the easiest music recommendation engine to integrate, has just released the V5 of its API with lots of new features. It includes:Search methods using sophisticated Elastic Search functionsIdentify the users most likely to like a given artistPersonalized recommendations of new...
    Musicovery, the easiest music recommendation engine to integrate, has just released the V4 of its API with lots of new features. It includes: the possibility to restrict results to a catalog of songs. the selection of the most appropriate activity radios for each time of the day and day of the...
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  • CONTACT

    You have great ideas. Let's talk.

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