Uploaded May 2021 | Updated September 2026, 2 weeks ago
Infinite Remixer is a Python application that creates remixes, patching together multiple songs at similar beats. To generate remixes, Infinite Remixer uses beat tracking and Nearest Neighbours search.
In the video, you’ll learn about the code of the system and its design, the rationale behind the project, how to use Infinite Remixer, my experiments with the system, the shortcomings I found and possible improvements.
Infinite Remixer on GitHub:
github.com/musikalkemist/infiniteremixer
Sweet Anticipation: Music and the Psychology of Expectation:
https://mitpress.mit.edu/books/sweet-anticipation
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Content:
0:00 Intro
0:37 Linear music consumption
2:20 Non-linear music consumption
3:33 The Eternal Jukebox
4:24 First look at Infinite Remixer
6:17 Segmentation component
7:14 Data component
11:22 Search component
12:35 Nearest Neighbours search
13:34 Remix component
17:52 How to use Infinite Remixer
21:17 Experiments with Infinite Remixer
21:40 Using chromograms
22:33 Using MFCCs
24:10 Experimenting with the "jump rate"
24:58 Problems with the system + possible improvements
27:05 Outro + project GitHub
27:51 Extended remix example
Infinite Remixer is a Python application that creates remixes, patching together multiple songs at similar beats. To generate remixes, Infinite Remixer uses beat tracking and Nearest Neighbours search.
In the video, you’ll learn about the code of the system and its design, the rationale behind the project, how to use Infinite Remixer, my experiments with the system, the shortcomings I found and possible improvements.
Infinite Remixer on GitHub:
github.com/musikalkemist/infiniteremixer
Sweet Anticipation: Music and the Psychology of Expectation:
https://mitpress.mit.edu/books/sweet-anticipation
===============================
Interested in hiring me as a consultant/freelancer?
valeriovelardo.com
Join The Sound Of AI Slack community:
valeriovelardo.com/the-sound-of-ai-community
Follow Valerio on Facebook:
facebook.com/TheSoundOfAI
Connect with Valerio on Linkedin:
linkedin.com/in/valeriovelardo
Follow Valerio on Twitter:
twitter.com/musikalkemist
===============================
Content:
0:00 Intro
0:37 Linear music consumption
2:20 Non-linear music consumption
3:33 The Eternal Jukebox
4:24 First look at Infinite Remixer
6:17 Segmentation component
7:14 Data component
11:22 Search component
12:35 Nearest Neighbours search
13:34 Remix component
17:52 How to use Infinite Remixer
21:17 Experiments with Infinite Remixer
21:40 Using chromograms
22:33 Using MFCCs
24:10 Experimenting with the "jump rate"
24:58 Problems with the system + possible improvements
27:05 Outro + project GitHub
27:51 Extended remix example
![This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]](https://i.ytimg.com/vi/zO49vZ31xb0/mqdefault.jpg "This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]
DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI agent that can carry out more than 600 tasks with a single transformer neural architecture. The tasks are varied, from playing Atari games to providing captions to images.
This paper demonstrates that:
📌 Generalist agents can perform reasonably well on many tasks / embodiments / modalities
📌 Generalist agents have the potential to learn new tasks with few data points
📌 By scaling up the parameter size, we can build a general-purpose agent
This work shocked me. I’ve always tackled AI from the perspective of Narrow Intelligence: build a specialised model that does well on a single - quite constrained - task.
👉 Gato paves the way for Artificial General Intelligence (AGI). In so doing, it opens new ethical dilemmas that should at least spark discussions in the AI community.
Since I’ve finished reading this paper, I can’t stop asking a question: is it ethical to push this research line given the grave dangers which may come with quasi-AGI agents?
Would you like to learn more? Check my last video, where I provide a breakdown of the paper, and analyse its ethical implications.
A Generalist Agent by DeepMind:
https://www.deepmind.com/publications/a-generalist-agent
Interested in hiring me as a consultant/freelancer?
https://valeriovelardo.com/
Join The Sound Of AI Slack community:
https://valeriovelardo.com/the-sound-of-ai-community/
Connect with Valerio on Linkedin:
https://www.linkedin.com/in/valeriovelardo
Follow Valerio on Facebook:
https://www.facebook.com/TheSoundOfAI
Follow Valerio on Twitter:
https://twitter.com/musikalkemist
Content:
0:00 Intro
1:11 General vs Narrow intellicence
3:06 Research hypotheses
4:25 Idea to approach AGI
8:23 Benefits of single network for many tasks
10:04 Datasets used
11:59 Data preparation
18:24 Model architecture
20:31 Training
22:48 Loss function
27:25 Recognising a task
30:50 Inference
33:37 How does the model perform?
39:00 Scale analysis
40:18 Can the model tackle unseen tasks?
44:14 Key discoveries
47:40 Ethical implications")
