Notochord: A Deep Probabilistic Model for Real-Time MIDI Performance

Core Concepts

Notochord is a deep probabilistic model designed for real-time MIDI performance, enabling interactive musical functions with low latency.

Abstract

Notochord is a novel deep probabilistic model developed for real-time MIDI performance. It allows interpretable interventions at a sub-event level, facilitating diverse interactive musical functions like generation, harmonization, and improvisation. The model can generate polyphonic and multi-track MIDI with latency below ten milliseconds. Notochord's design emphasizes low-latency processing to ensure imperceptible delays between user actions and system responses. By training on the Lakh MIDI dataset, Notochord offers open-source software for exploring creative AI in real-time music performance settings. The paper discusses the challenges of integrating deep learning models into performance settings where instantaneous feedback is crucial. It highlights the importance of studying creative AI in musical domains to understand unique interactions between users and intelligent systems. Notochord's design aims to bridge the gap between very low latency requirements in music applications and the diversity of potential use cases it enables. The content delves into the technical aspects of Notochord, including its autoregressive factorization approach, sub-event order modeling, sub-event distributions using mixture models, and neural network architecture implementation. The paper also presents results from training Notochord on the Lakh MIDI dataset and provides insights into its efficacy in generating musical sequences with varying levels of information available. Overall, Notochord represents a significant advancement in leveraging deep learning for real-time music performance applications by offering a flexible and interpretable model that responds instantaneously to user inputs.

Stats

Latency below ten milliseconds Trained on Lakh MIDI dataset Open-source software provided

Quotes

"Notochord enables interpretable interventions at a sub-event level." "Notochord bridges the gap to very low latency requirements in music applications."

Key Insights Distilled From

Notochord

by Victor Shepa... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12000.pdf

Deeper Inquiries

How does Notochord compare to existing models in terms of real-time responsiveness

Notochord stands out from existing models in terms of real-time responsiveness due to its low-latency processing capabilities. The design of Notochord emphasizes instantaneous feedback, with a latency below ten milliseconds, making it ideal for interactive music experiences where user actions need to feel immediate. This sets it apart from many other creative AI applications that often involve delays on the order of seconds or longer between action and result. By using a causal and order-agnostic event representation, Notochord can process each input MIDI event quickly and efficiently, enabling seamless interaction in real-time performance settings.

What are the implications of using Notochord for live performances or interactive music experiences

Using Notochord for live performances or interactive music experiences has significant implications for enhancing the embodied experience of AI in musical instruments. By allowing fine-grained interventions at a sub-event level, Notochord enables performers to interact with the model in real-time, creating a dynamic partnership between human creativity and machine intelligence. This opens up possibilities for steerable generation, harmonization based on player inputs, machine improvisation that responds to live performances, and likelihood-based interfaces that offer unique ways to engage with the model during a performance. Overall, Notochord's flexibility and low-latency processing make it well-suited for exploring new forms of musical expression through technology.

How can Notochord's design principles be applied to other domains beyond music

The design principles underlying Notochord can be applied beyond music to various other domains where real-time probabilistic modeling is required. For instance: Interactive Art Installations: Artists could use similar models to create interactive installations that respond dynamically to audience interactions. Gaming: Game developers could implement probabilistic models like Notochord for generating adaptive gameplay elements based on player actions. Healthcare: Real-time probabilistic models could be used in healthcare settings for monitoring patient data streams and providing instant feedback or predictions. Finance: Applications in finance could benefit from responsive probabilistic models for analyzing market trends or risk assessment in real time. By adapting the principles of low-latency processing and interpretable interventions across different domains, similar systems can enhance user experiences by offering immediate responses tailored to specific contexts or inputs.