Tenor Christian Joel, bass Frederick Tong, mezzo-soprano Joanna Gamble and soprano Olivia Doutney.

Another machine learning model lets the blobs respond to and harmonise your input in real time. Or forwards and backwards for different vowel sounds. In the experiment you don’t hear their voices, but the machine learning model’s understanding of what opera singing sounds like, based on what it learnt from them.ĭrag the blobs up and down to change pitch. Tenor, Christian Joel, bass Frederick Tong, mezzo-soprano Joanna Gamble and soprano Olivia Doutney recorded 16 hours of singing. We developed a machine learning model trained on the voices of four opera singers in order to create an interactive experiment anyone can enjoy, regardless of musical skills. This experiment pays tribute to and explores the original musical instrument: the voice. To really utilize the technology, many more researches about behavioral and neuroimaging modelling are needed.įor Google, the four singing blobs are just the start, and it is its way to end the sorrowful 2020 with a little joy.Blob Opera is a machine learning experiment by David Li in collaboration with Google Arts and Culture. This kind of capability could change the AI music revolution. To do this, Blob Opera uses a second machine learning model to make the blobs respond to and harmonize users input in real time. This kind of feat requires a large dataset of existing harmonies, fed into machine-learning algorithm that can find patterns in that data. What makes the blobs smart, is the way the AI harmonizes the voices in real-time to any new directions. When users drag the blobs, the blob with the highest voice will always take the melody while the bassier blobs will do the harmonious groundwork for their star soloist. Users can run a simple pitch tracker of their voice in real-time, and then use the harmonization component from Blob Opera. For example, users can use their own voice to voice the blobs.

Google blob opera