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Flow Machines: AI-Powered Music Composition Tool

Flow Machines

Flow Machines uses cutting-edge machine learning to help musicians create new music. Compose melodies, customize parameters, and export MIDI files for seamless DAW integration.

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Flow Machines: AI-Powered Music Composition Tool

Flow Machines: Augmenting Creativity with AI

Flow Machines is a research, development, and social implementation project that aims to expand the creativity of creators in music. Throughout the history of music, inventions and technological developments in each era have expanded the creativity of creators, leading to new stages in music. Flow Machines uses cutting-edge machine learning technology to create new music with creators.

Key Features

  • Compose melodies according to the creator's intentions: Specify the style and chord progression, and the AI will analyze various musical pieces and propose a 4-bar or 8-bar melody based on the established musical rules.
  • Original style palette: Train Flow Machines with your own melodies to create an original style palette. You can generate melodies that feel like yours but are not exactly the same.
  • Over 100 preset style palettes: The AI will provide suggestions based on over 100 style palettes, including "POP" and "EDM." There are infinite combinations of melodies that can be suggested from a single style palette.
  • Adjustment function with composition parameters:
    • Harmony: The degree of match between chords and melodies
    • Duration: Note length
    • Complexity: Complexity of the melody
  • Favorite save function: You can save your favorite melodies suggested by the AI.
  • Recompose specific measures: You can make fine adjustments, such as recomposing only two measures out of eight while maintaining the other measures.
  • MIDI export function: You can export your favorite melodies using the Export MIDI button and import them into your DAW to incorporate them into your music immediately.
  • MIDI editing function: Melodies transferred to the DAW using the MIDI export function can be freely edited as MIDI data, allowing you to customize the sound to your liking.

System Requirements

  • Mac OS: macOS Big Sur 11.0 or later
  • Tested DAWs: Cubase Pro 11/12, Studio One 5, Ableton Live 11, Digital Performer, Logic Pro, GarageBand, Pro Tools

Getting Started with Flow Machines

Download the app from the App Store and experience new music production with Flow Machines!

iPad Version

Flow Machines is also available for iPad. The operation method is the same as for Mac OS. With the portability of an iPad, you can easily create melodies anytime, anywhere.

News

  • New Dark Pop and Doo-Wop Pop palettes added: Two new style palettes, "Dark Pop" and "Doo-Wop Pop," have been released. (2024/10/08)
  • Flow Machines update & new GANStrument feature: The iOS mobile app "Flow Machines Mobile" has been updated with a new feature, "GANStrument." (2024/08/08)
  • Flow Machines update & new style palette added: A new style palette, "Ambient2," has been added to Flow Machines. (2024/02/09)
  • InterBEE 2023 Special Lecture: A special lecture titled "New Sounds and Music Created by AI and Machine Learning" was held at Inter BEE 2023. (2023/12/01)
  • Logic Pro for iPad support and article published in DTM Station: Flow Machines ver 1.1.7 has been released, enabling use on Logic Pro for iPad. (2023/10/13)
  • New Reggae style palette added: A new "Reggae" palette has been added to Flow Machines. (2023/05/25)
  • Spring2 style palette released: The second "Spring" palette, "Spring2," focusing on Japanese rock styles, has been added. (2023/03/31)
  • World premiere performance of PIA at "Human Code Ensemble": The Sony CSL-developed piano composition tool PIA was performed for the first time at the "Human Code Ensemble." (2023/03/02)

© 1993-2024 Sony Computer Science Laboratories, Inc., Tokyo, Japan. "FLOW MACHINES" and "フローマシーンズ" are trademarks of Sony Corporation. "FLOW MACHINES" and "フローマシーンズ" are technologies developed by Sony Computer Science Laboratories.

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement no. 291156). The research leading to these results has been conducted by Sony Computer Science Laboratories Paris (Sony CSL Paris) and Pierre and Marie Curie University (UPMC).

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