Polymath

Polymath is a music production tool that uses machine learning to convert any music library into a sample library. It automatically separates songs into stems, quantizes them to the same tempo and beat-grid, analyzes musical structure and key, and converts audio to MIDI. This results in a searchable sample library that streamlines the workflow for music producers, DJs, and ML audio developers.

Key Features:

1. Music Source Separation: Polymath uses the Demucs neural network to separate songs into stems, such as beats, bass, and vocals.

2. Music Structure Segmentation/Labeling: The sf_segmenter neural network is used to analyze the musical structure of songs, identifying elements like verses and choruses.

3. Music Pitch Tracking and Key Detection: Polymath utilizes the Crepe neural network to track the pitch of music and detect the key it is in.

Use Cases:

• Music Composition: Polymath makes it effortless to combine elements from different songs to create unique compositions. Users can easily grab a beat from one track, a bassline from another, and fitting horns from a third, seamlessly integrating them into their digital audio workstation (DAW).

• DJ Sets: With Polymath's search capability, DJs can quickly discover related tracks and create polished, hour-long mash-up DJ sets.

• ML Audio Development: Polymath simplifies the process of creating a large music dataset for training generative models by automatically converting audio files into MIDI and providing a searchable sample library.

Conclusion:

Polymath is a powerful tool for music producers, DJs, and ML audio developers. Its machine learning capabilities enable users to convert music libraries into sample libraries, separate songs into stems, analyze musical structure and key, and convert audio to MIDI. With its ease of use and practical applications in music composition, DJ sets, and ML audio development, Polymath streamlines workflows and enhances creativity in the music industry.