Foundation Models for Music: A Survey
(2408.14340)Abstract
In recent years, foundation models (FMs) such as LLMs and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.
Overview
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The paper 'Foundation Models for Music: A Survey' provides a comprehensive review of the advancements in applying foundation models (FMs) to music, including LLMs and latent diffusion models (LDMs), addressing music understanding, generation, and applications through self-supervised learning on large unlabelled datasets.
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It discusses various music representations, the importance of symbolic and acoustic forms, and the underdeveloped area of multimodal integrations, detailing music understanding and generation tasks such as melody extraction, genre classification, and automated music composition using models like MERT, MusicFM, and MusicGen.
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Technical aspects covered include pre-training strategies like contrastive learning and masked modelling, domain adaptation techniques such as few-shot and zero-shot learning, and model architectures with a focus on transformer-based designs, alongside ethical considerations about data bias, fairness, and cultural implications.
Overview of the Paper "Foundation Models for Music: A Survey"
The paper "Foundation Models for Music: A Survey" presents a comprehensive review of recent advances in the application of foundation models (FMs) to music. Authored by researchers from institutes such as Queen Mary University of London and Carnegie Mellon University, the document delineates the evolving landscape of music-related pre-trained models and foundation models, including LLMs and latent diffusion models (LDMs). These models aim to address the significant technical challenges inherent in music understanding, generation, and application, leveraging self-supervised learning on vast, unlabelled datasets.
Key Sections and Insights
The paper is divided into several key sections, each providing detailed insights into different facets of the research on foundation models for music.
Representations of Music
One of the primary discussions in the paper revolves around various representations of music, emphasizing the importance of both symbolic and acoustic representations. Unlike other fields, music data is unique in that it can be represented in multiple forms, from raw audio waveforms to structured symbolic data such as MIDI and MusicXML. Foundation models leverage these representations to capture diverse musical concepts, ranging from basic audio features like pitch and rhythm to complex structures involving harmonics and tempo. The section underscores that while audio-based models like those using Mel spectrograms are well-explored, symbolic representations and multimodal integrations remain relatively underdeveloped in FM research.
Applications in Music Understanding and Generation
The applications of FMs in music, as delineated in the paper, are extensive. Key areas include music understanding tasks like melody extraction, genre classification, and music information retrieval (MIR), as well as music generation tasks covering both symbolic and acoustic domains.
Music Understanding
Large language models pre-trained on music data have demonstrated significant progress in capturing musical structure and properties. Models like MERT (Music Embedding Representation trained with Teacher) and MusicFM have shown state-of-the-art performance on various MIR tasks, affirming the potential of FMs to generalize across diverse music understanding applications. The MARBLE benchmark provides a unified testbed for evaluating the performance of these models.
Music Generation
For music generation, models such as MusicGen and MusicLM exploit transformer architectures and autoregressive predictive coding to generate high-quality music, conditioned on text prompts or other musical inputs. These models exhibit notable performance in generating coherent musical pieces, thereby broadening the scope of automated music composition tools. Recent advances in LDMs, like those in AudioLDM, offer promising directions for high-fidelity audio generation.
Technical Aspects of Foundation Models
The paper provides an in-depth technical discussion on pre-training strategies and model architectures, highlighting the approach of self-supervised learning which underpins many FMs. It covers paradigms such as contrastive learning, masked modelling, and generative pre-training.
Pre-Training Paradigms
Contrastive learning, exemplified by models like MuLan and CLAP, uses instance-level or context-level representations to align multimodal data (e.g., audio and text). Similarly, masked modelling approaches like w2v-BERT and HuBERT facilitate the learning of robust representations by predicting masked input tokens in the audio sequence. These techniques are essential for capturing the intricate patterns in music.
Domain Adaptation Techniques
Domain adaptation techniques like few-shot and zero-shot learning enable FMs to adapt to specific music tasks with minimal additional data. Methods such as prefix tuning and adapter tuning are discussed in the context of how they can be leveraged to improve the performance and versatility of music FMs. The importance of instruction tuning and in-context learning is also emphasized, although these areas remain relatively unexplored in the music domain.
Model Architectures
Transformer-based architectures are predominant in the design of FMs, given their ability to handle sequential data effectively. The paper discusses advances in scaling laws and emergent abilities, noting that the capability of FMs to generalize depends significantly on the size of the model and the quantity and quality of training data.
Datasets and Evaluation
The paper catalogues a wide array of datasets essential for training music FMs, including both symbolic and audio datasets. Examples include the MAESTRO dataset for piano music and the Free Music Archive for diverse audio tracks. The evaluation section details methodologies for assessing music understanding and generation tasks, stressing the importance of robust benchmarks like MARBLE.
Ethical and Social Implications
The paper does not shy away from addressing the ethical considerations surrounding the deployment of FMs in music. Issues such as data bias, fairness, and transparency are discussed alongside the challenges related to copyright infringement. The ethical section underscores the need for responsible AI development to avoid cultural homogenization and ensure the fair use of musical data.
Conclusion
In conclusion, the paper "Foundation Models for Music: A Survey" offers a thorough exploration of the state-of-the-art in applying FMs to music, highlighting significant advancements and outlining the challenges that remain. It serves as a crucial resource for researchers and practitioners aiming to leverage AI to enhance music understanding and generation, pointing towards a future where foundation models play a central role in the musical landscape. The document advocates for continued interdisciplinary collaboration to address the technical, ethical, and cultural challenges in this vibrant and evolving field.
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