We use two sources of frisson moments: public online comments and an in-house listening team. This proprietary dataset of over a million moments enables us to train artificial intelligence algorithms that can identify moments in new songs.
There are two core algorithms (AI models) available to users on the site. Each algorithm draws upon artificial intelligence techniques from an advanced branch of machine learning called “deep learning” that mimics the way the human brain identifies and learns patterns.
The Qbrio AI discovered nine, related sets of frisson-causing features. These patterns kept appearing across frisson moments, which we then validated with acoustics experts and evolutionary biologists.
This feature enables users to upload files and receive analyses from the Qbrio AI.
When you upload a song, the Qbrio AI predicts frisson at each second of your upload and delivers this analysis via a heatmap visual. These heatmaps and core metrics are the common units of analysis across all of the Qbrio site features.
This is the default view for in-depth analysis and editing of one song. After viewing your initial analytics, you can make edits and re-upload the song to see if your ranking increases and where your frisson scores change.
This features lets you compare up to four mixes of a song. The view supports synced play and toggling between tracks just like a DAW. Creative teams use this view to help avoid the common problem of “going past” the best mix of a song.
This feature enables users to compare up to 20 songs at a time. A&R teams use this view to scout new talent and to decide which tracks to cut as singles off of new albums from in-house talent.
This feature enables users to search the Qbrio dataset of verified listener frisson moments for creative inspiration.