Coherence-Oriented Contrastive Learning for Generating Coherent Musical Audio Representations

Core Concepts

COCOLA is a contrastive learning method that captures the harmonic and rhythmic coherence between musical audio samples, enabling the objective evaluation of compositional models for music accompaniment generation.

Abstract

The paper presents COCOLA (Coherence-Oriented Contrastive Learning for Audio), a novel contrastive learning method for musical audio representations. COCOLA operates at the level of stems (or their combinations) composing music tracks and allows the objective evaluation of compositional models for music accompaniment generation. The key highlights are: COCOLA maximizes the agreement between disjoint sub-components (sub-mixtures of stems) within the same audio window, and minimizes it on sub-components belonging to different windows. This allows the model to capture the harmonic and rhythmic coherence between audio samples. The authors introduce a new baseline for compositional music generation called CompoNet, based on ControlNet. CompoNet can perform a wide range of compositional tasks, including accompaniment generation, source separation, and editing, in a unified framework. The authors release all models trained on public datasets containing separate stems (MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales), enabling further research in this area. The COCOLA score is proposed as a metric to quantify the coherence between conditioning tracks and generated accompaniments. Experiments show that CompoNet outperforms the previous MSDM model in accompaniment generation, as measured by the COCOLA score.

Stats

The paper uses the following public datasets for training and evaluation: MUSDB18-HQ: 150 tracks with 4 stems (Bass, Drums, Vocals, Other) MoisesDB: 240 tracks with 11 stems Slakh2100: 2100 tracks with 34 stems CocoChorales: 4000 tracks with 13 stems

Quotes

"COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples." "We also introduce a new baseline for compositional music generation called CompoNet, based on ControlNet [1], generalizing the tasks of MSDM, and quantify it against the latter using COCOLA."

Key Insights Distilled From

COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations

by Rube... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16969.pdf

Deeper Inquiries

How could COCOLA be extended to incorporate additional modalities, such as text or symbolic music representations, to further improve the coherence of generated musical accompaniments?

To incorporate additional modalities like text or symbolic music representations into COCOLA for improved coherence in generated musical accompaniments, a multimodal approach can be adopted. This would involve modifying the encoder architecture to handle multiple input modalities simultaneously. For text, a separate text encoder can be integrated into the model to process textual descriptions or instructions related to the music. This text encoder can be trained jointly with the audio encoder to learn a shared representation space where both modalities can interact. For symbolic music representations, such as MIDI or sheet music, the model can be extended to include a symbolic music encoder that can process the symbolic representation of the music tracks. By incorporating these additional modalities, the model can learn to capture the semantic relationships between different modalities and generate more coherent accompaniments that align with both the textual instructions and the symbolic music representations.

How could the COCOLA training procedure be adapted to handle variable-length audio inputs and outputs, to better reflect real-world music composition workflows?

To adapt the COCOLA training procedure to handle variable-length audio inputs and outputs, several modifications can be made to better reflect real-world music composition workflows. One approach is to implement a windowing mechanism that can process audio inputs of varying lengths by segmenting them into fixed-size windows during training. This would allow the model to handle audio inputs of different durations without requiring them to be of uniform length. Additionally, the contrastive learning framework can be extended to consider temporal dependencies across multiple windows to capture long-range coherence in the music. By incorporating mechanisms like self-attention or recurrent neural networks, the model can learn to maintain coherence and continuity across variable-length audio segments. Furthermore, the training procedure can be augmented with techniques like data augmentation and padding to handle variable-length outputs during generation. By incorporating these adaptations, COCOLA can better accommodate the complexities of real-world music composition workflows where audio inputs and outputs may vary in duration and structure.

What other applications beyond accompaniment generation could benefit from the coherence-oriented representations learned by COCOLA?

The coherence-oriented representations learned by COCOLA can have applications beyond accompaniment generation in various domains: Music Remixing: COCOLA's ability to capture harmonic and rhythmic coherence can be leveraged in music remixing tasks where different parts of a song need to be rearranged or combined while maintaining musical coherence. Music Production: In music production, COCOLA's representations can assist in mixing and mastering processes by ensuring that different tracks and stems blend well together to create a cohesive and balanced sound. Music Analysis: The learned representations can be valuable for music analysis tasks such as genre classification, mood detection, and similarity matching, where capturing the underlying coherence between musical elements is crucial for accurate analysis. Interactive Music Systems: COCOLA's coherence-oriented representations can enhance interactive music systems that respond to user inputs in real-time, ensuring that the generated music remains coherent and musically pleasing throughout the interaction. Music Recommendation: By understanding the coherence between different musical elements, COCOLA can improve music recommendation systems by recommending songs or tracks that are musically coherent with the user's preferences or current listening context. Overall, the coherence-oriented representations learned by COCOLA have the potential to benefit a wide range of music-related applications beyond accompaniment generation, where maintaining musical coherence is essential for high-quality output.