Instant Mix for Music Beta by Google
June 8th, 2011 | Published in Google Research
Music Beta by Google was announced at the Day One Keynote of Google I/O 2011. This service allows users to stream their music collections from the cloud to any supported device, including a web browser. It’s a first step in creating a platform that gives users a range of compelling music experiences. One key component of the product, Instant Mix, is a playlist generator developed by Google Research. Instant Mix uses machine hearing to extract attributes from audio which can be used to answer questions such as “Is there a Hammond B-3 organ?” (instrumentation / timbre), “Is it angry?” (mood), “Can I jog to it?” (tempo / meter) and so on. Machine learning algorithms relate these audio features to what we know about music on the web, such as the fact that Jimmy Smith is a jazz organist or that Arcade Fire and Wolf Parade are similar artists. From this we can predict similar tracks for a seed track and, with some additional sequencing logic, generate Instant Mix playlists from songs in a user’s locker.
Because we combine audio analysis with information about which artists and albums go well together, we can use both dimensions of similarity to compare songs. If you pick a mellow track from an album, we will make a mellower playlist than if you pick a high energy track from the same album. For example, here we compare short Instant Mixes made from two very different tracks by U2. The first Instant Mix comes from "Mysterious Ways," an upbeat, danceable track from Achtung Baby with electric guitar and heavy percussion.
- U2 "Mysterious Ways"
- David Bowie "Fame"
- Oingo Boingo "Gratitude"
- Infectious Grooves “Spreck”
- Red Hot Chili Peppers “Special Secret Song Inside”
Compare this to a short Instant Mix made from a much more laid back U2 cut, "MLK" from the album Unforgettable Fire. This track has delicate vocals on top of a sparse synthesizer background and no percussion.
- U2 "MLK"
- Jewel “Don’t”
- Antony and the Johnsons “What Can I Do?”
- The Beatles “And I Love Her”
- Van Morrison “Crazy Love”
Our approach also allows us to create mixes from music in the long tail. Are you the lead singer in an unknown Dylan cover band? Even if your group is new or otherwise unknown, Instant Mix can still use audio similarity to match your tracks to real Dylan tracks (provided, of course, that you sing like Bob and your band sounds like The Band).
Our goal with Instant Mix is to build awesome playlists from your music collection. We achieve this by using machine learning to blend a wide range of information sources, including features derived from the music audio itself. Though we’re still in beta, and still have a lot of work to do, we believe Instant Mix is a great tool for music discovery that stands out from the crowd. Give it a try!
Further reading by Google Researchers:
Machine Hearing: An Emerging Field
Richard F. Lyon.
Sound Ranking Using Auditory Sparse-Code Representations
Martin Rehn, Richard F. Lyon, Samy Bengio, Thomas C. Walters, Gal Chechik.
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces
Jason Weston, Samy Bengio, Philippe Hamel.
Our goal with Instant Mix is to build awesome playlists from your music collection. We achieve this by using machine learning to blend a wide range of information sources, including features derived from the music audio itself. Though we’re still in beta, and still have a lot of work to do, we believe Instant Mix is a great tool for music discovery that stands out from the crowd. Give it a try!
Further reading by Google Researchers:
Machine Hearing: An Emerging Field
Richard F. Lyon.
Sound Ranking Using Auditory Sparse-Code Representations
Martin Rehn, Richard F. Lyon, Samy Bengio, Thomas C. Walters, Gal Chechik.
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces
Jason Weston, Samy Bengio, Philippe Hamel.