insight - Video game music - # Symbolic music generation from gameplay videos

A Dataset of Symbolic Video Game Music Paired with Gameplay Videos from the Nintendo Entertainment System

Q: How can the genre classification performance be improved to better evaluate the generated music

To improve the genre classification performance for evaluating the generated music, several strategies can be implemented. Firstly, increasing the size and diversity of the training data for the genre classifier can enhance its ability to generalize and accurately predict genres. This can involve incorporating additional labeled data from various sources to cover a wider range of genres and musical styles. Furthermore, fine-tuning a pre-trained music model specifically designed for genre classification can leverage existing knowledge and patterns in music genres to improve classification accuracy. Additionally, feature engineering techniques such as extracting more informative features from the symbolic music data, such as chord progressions, tempo variations, or melodic motifs, can provide the classifier with richer input for genre prediction. Lastly, exploring ensemble learning methods that combine multiple genre classifiers or leveraging advanced deep learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can potentially enhance the classifier's performance by capturing complex patterns and dependencies in the music data.

Q: What other game data (e.g., tile maps, enemy movements) could be used to condition the music generation, and how would that affect the quality of the generated pieces

In addition to gameplay videos and background music, incorporating other game data such as tile maps, enemy movements, player actions, or game events could significantly impact the quality and relevance of the generated music pieces. By conditioning the music generation process on a broader range of game data, the generated music can better reflect the dynamic and interactive nature of the gameplay. For example, using tile maps could influence the harmonic progression or tonality of the music based on the game environment or level design. Enemy movements and player actions could dictate the rhythm, tempo, or intensity of the music, creating a more immersive and responsive soundtrack. Game events like boss battles, level completions, or power-ups could trigger musical variations or transitions, enhancing the overall gaming experience. By integrating multiple facets of game data into the music generation process, the generated pieces can be more contextually relevant, adaptive, and engaging for players.

Q: What other applications, beyond game music generation, could the NES-VMDB dataset enable, such as music analysis or music information retrieval tasks

The NES-VMDB dataset opens up a wide range of potential applications beyond game music generation, including music analysis and music information retrieval tasks. One application could be in music recommendation systems, where the dataset's paired gameplay videos and background music could be leveraged to recommend music tracks based on a user's gaming preferences or playing style. Music similarity and clustering algorithms could be applied to analyze the musical characteristics of the dataset and identify patterns or trends in NES game music. Furthermore, the dataset could be used for music transcription tasks, converting audio tracks from gameplay videos into symbolic music notation for further analysis or remixing. Additionally, researchers in the field of music information retrieval could utilize the dataset to develop algorithms for automatic music tagging, genre classification, or music similarity metrics tailored to video game music. Overall, the NES-VMDB dataset provides a rich resource for exploring the intersection of music and gaming, offering diverse opportunities for research and innovation in music-related applications.

Core Concepts

The NES Video-Music Database (NES-VMDB) is a novel dataset containing 98,940 gameplay videos from 389 NES games, each paired with its original soundtrack in symbolic format (MIDI). The dataset aims to enable the development of generative models that can compose music conditioned on gameplay videos.

Abstract

The NES Video-Music Database (NES-VMDB) is an extension of the Nintendo Entertainment System Music Database (NES-MDB), which contains 5,278 music pieces from 397 NES games. The NES-VMDB associates 4,070 of these pieces with short gameplay clips from the game scenes where the music is played.

To create the NES-VMDB, the authors first obtained long-play videos for 389 NES games from YouTube. They divided each video into 15-second clips and extracted the audio. Then, they used an audio fingerprinting algorithm (similar to Shazam) to automatically identify the corresponding MIDI piece from the NES-MDB dataset for each audio clip.

The authors also established a baseline generative model based on the Controllable Music Transformer (CMT) to generate NES music conditioned on gameplay videos. They trained the CMT model on the NES-VMDB MIDI pieces and generated new music by conditioning it with rhythmic features extracted from the gameplay clips.

The authors evaluated the generated music using objective metrics related to music structure, such as pitch class histogram entropy, grooving pattern similarity, pitch range, and number of notes played concurrently. The results showed that the conditional CMT model generated music that was more structurally similar to human-composed pieces compared to the unconditional model.

Additionally, the authors trained a neural classifier to predict the game genre of the generated pieces. The results indicated that the conditional CMT model was able to learn correlations between gameplay videos and game genres, but further research is needed to achieve human-level performance.

The NES-VMDB dataset and the baseline CMT model provide a foundation for future research on generating video game music from gameplay data, with the goal of supporting indie game developers in creating music for their projects.

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Stats

The NES-VMDB dataset contains 98,940 gameplay videos from 389 NES games, with an average of 225.32 clips per game (standard deviation of 501.39).
The NES-MDB dataset, which the NES-VMDB is built upon, contains 5,278 music pieces from 397 NES games.

Quotes

"Our objective is to enable music generation models that allow users to input a brief gameplay clip of their own game and receive background music that complements the scene pictured in the clip."
"We envision these generators being utilized by indie game developers to generate music in different stages of their projects, from musical sketches at early stages to final soundtracks at later versions of the game."

Key Insights Distilled From

The NES Video-Music Database

by Igor Cardoso... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04420.pdf

Deeper Inquiries

How can the genre classification performance be improved to better evaluate the generated music

To improve the genre classification performance for evaluating the generated music, several strategies can be implemented. Firstly, increasing the size and diversity of the training data for the genre classifier can enhance its ability to generalize and accurately predict genres. This can involve incorporating additional labeled data from various sources to cover a wider range of genres and musical styles. Furthermore, fine-tuning a pre-trained music model specifically designed for genre classification can leverage existing knowledge and patterns in music genres to improve classification accuracy. Additionally, feature engineering techniques such as extracting more informative features from the symbolic music data, such as chord progressions, tempo variations, or melodic motifs, can provide the classifier with richer input for genre prediction. Lastly, exploring ensemble learning methods that combine multiple genre classifiers or leveraging advanced deep learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can potentially enhance the classifier's performance by capturing complex patterns and dependencies in the music data.

What other game data (e.g., tile maps, enemy movements) could be used to condition the music generation, and how would that affect the quality of the generated pieces

In addition to gameplay videos and background music, incorporating other game data such as tile maps, enemy movements, player actions, or game events could significantly impact the quality and relevance of the generated music pieces. By conditioning the music generation process on a broader range of game data, the generated music can better reflect the dynamic and interactive nature of the gameplay. For example, using tile maps could influence the harmonic progression or tonality of the music based on the game environment or level design. Enemy movements and player actions could dictate the rhythm, tempo, or intensity of the music, creating a more immersive and responsive soundtrack. Game events like boss battles, level completions, or power-ups could trigger musical variations or transitions, enhancing the overall gaming experience. By integrating multiple facets of game data into the music generation process, the generated pieces can be more contextually relevant, adaptive, and engaging for players.

What other applications, beyond game music generation, could the NES-VMDB dataset enable, such as music analysis or music information retrieval tasks

The NES-VMDB dataset opens up a wide range of potential applications beyond game music generation, including music analysis and music information retrieval tasks. One application could be in music recommendation systems, where the dataset's paired gameplay videos and background music could be leveraged to recommend music tracks based on a user's gaming preferences or playing style. Music similarity and clustering algorithms could be applied to analyze the musical characteristics of the dataset and identify patterns or trends in NES game music. Furthermore, the dataset could be used for music transcription tasks, converting audio tracks from gameplay videos into symbolic music notation for further analysis or remixing. Additionally, researchers in the field of music information retrieval could utilize the dataset to develop algorithms for automatic music tagging, genre classification, or music similarity metrics tailored to video game music. Overall, the NES-VMDB dataset provides a rich resource for exploring the intersection of music and gaming, offering diverse opportunities for research and innovation in music-related applications.