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MidiTok Visualizer: a tool for visualization and analysis of tokenized MIDI symbolic music (2410.20518v1)

Published 27 Oct 2024 in cs.SD, cs.AI, cs.MM, and eess.AS

Abstract: Symbolic music research plays a crucial role in music-related machine learning, but MIDI data can be complex for those without musical expertise. To address this issue, we present MidiTok Visualizer, a web application designed to facilitate the exploration and visualization of various MIDI tokenization methods from the MidiTok Python package. MidiTok Visualizer offers numerous customizable parameters, enabling users to upload MIDI files to visualize tokenized data alongside an interactive piano roll.

Summary

  • The paper introduces MidiTok Visualizer, a web-based tool that facilitates interactive visualization and comparison of diverse MIDI tokenization techniques.
  • It employs an intuitive piano roll display with detailed musical metrics, making complex MIDI data accessible to both novice and expert users.
  • The tool’s modular design—built with FastAPI, React, and Docker—supports robust performance, community collaboration, and future enhancements in symbolic music research.

MidiTok Visualizer: Enhancing MIDI Tokenization Exploration

The paper "MidiTok Visualizer: A Tool for Visualization and Analysis of Tokenized MIDI Symbolic Music" presents a noteworthy contribution to the field of symbolic music research, addressing complexities faced by researchers exploring MIDI data. Given the challenges in interpreting MIDI data due to its inherent complexity and the lack of musical training among many AI researchers, the researchers introduce MidiTok Visualizer—a web-based application designed to bridge this gap by facilitating intuitive exploration of MIDI tokenization methods derived from the MidiTok Python package.

Key Functionalities

MidiTok Visualizer operates as an interactive web application offering several core functionalities. Primarily, it enables users to upload MIDI files, providing graphical representations of the generated tokens using various tokenization techniques. The interface furnishes an intuitive environment for probing the structure and content of MIDI data, providing substantial utility for both novice and expert users. The application allows for dynamic experimentation with different tokenization settings, elucidating the impact of specific parameters on the resulting tokenization.

A significant feature of the tool is the piano roll visualization, which aligns with the token stream, highlighting relevant tokens in correspondence with their musical notes. This interactive element is supplemented by the display of key musical metrics, including key signatures, time signatures, tempo, and pitch range, fostering a comprehensive understanding of the musical composition.

Supported Tokenizers

The application supports various tokenizers, such as CPWord, MIDI-Like, Octuple, REMI, Structured, and TSD, each with configurable parameters, per the documentation of the original MidiTok package. This functionality permits sophisticated analysis and comparison of tokenization methods, underpinning further research in the domain.

System Design

MidiTok Visualizer employs a modular architecture, utilizing FastAPI for back-end operations and React for the front-end, ensuring a robust and responsive experience. Supporting technologies include Pydantic for data modeling, MusPy for MIDI processing, and Docker for seamless deployment. Dependency management and testing are streamlined with Poetry and Pytest respectively, enhancing the application's development efficiency and reliability.

Licensing and Community Collaboration

Released under the GPL license, MidiTok Visualizer is open for community contribution. This openness invites collaborative enhancement, encouraging contributions of new features, bug fixes, and improved functionalities, supported by its public GitHub repository.

Implications and Future Work

MidiTok Visualizer represents an important tool in lowering the barriers associated with symbolic music research, offering practical insights into MIDI tokenization. Its design serves as a foundational platform for further advancements, potentially contributing to improved AI models for music generation and analysis. Future development directions include the integration of additional tokenizers, managing complex tokenization scenarios, and expanding visualization and editing capabilities. These enhancements would further solidify the tool's applicability and relevance within the symbolic music research community.

The paper underscores a critical intersection of music and machine learning, positing MidiTok Visualizer as a key facilitator in advancing understanding and analysis of MIDI data. As symbolic music research continues to evolve, tools like MidiTok Visualizer will play a crucial role in expanding accessibility and fostering innovation in the field.

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