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1
Intro
2
Linear music consumption
3
Non-linear music consumption
4
The Eternal Jukebox
5
First look at Infinite Remixer
6
Segmentation component
7
Data component
8
Search component
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Nearest Neighbours search
10
Remix component
11
How to use Infinite Remixer
12
Experiments with Infinite Remixer
13
Using chromograms
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Using MFCCs
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Experimenting with the "jump rate"
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Problems with the system + possible improvements
17
Outro + project GitHub
18
Extended remix example
Description:
Explore the creation of automatic song remixes using audio signal processing and machine learning in this 30-minute video tutorial. Learn about the Infinite Remixer Python application, which generates remixes by patching together multiple songs at similar beats using beat tracking and Nearest Neighbours search. Dive into the system's code, design rationale, and usage instructions. Discover experiments conducted with the system, including the use of chromograms and MFCCs, as well as adjusting the "jump rate." Examine the shortcomings of the current implementation and potential improvements. Gain insights into linear and non-linear music consumption, and understand the concept behind projects like The Eternal Jukebox. Access the Infinite Remixer GitHub repository and explore additional resources on music psychology and expectation.

Automatic Song Remixes With Audio Signal Processing and Simple Machine Learning

Valerio Velardo - The Sound of AI
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