Music Foundation Models Overview

• Music Foundation Models (MFMs) are large-scale, general-purpose neural architectures that perform comprehensive music analysis, generation, and cross-modal understanding.
• They utilize self-supervised learning paradigms like masked modeling on tokenized music signals, unifying diverse music information retrieval and generation tasks.
• MFMs scale efficiently on extensive datasets to support varied MIR tasks while addressing emerging ethical and cultural challenges.

Music Foundation Models (MFMs) are large-scale, general-purpose neural architectures for music data, designed to encode, analyze, generate, and enable cross-modal understanding of music via self-supervised or weakly supervised pretraining on extensive and heterogeneous corpora. MFMs unify a range of previously siloed music information retrieval (MIR) and generation tasks within a single framework, exhibiting emergent capabilities in multi-tasking, transferability, and music-specific reasoning through principled representation learning, scalable training, and task adaptation. These models redefine the computational paper of music by enabling both compositional and analytic tasks from limited labeled data, supporting multimodal applications, and introducing new challenges in ethical and cultural domains.

1. Foundations: Pretraining Paradigms, Architectures, and Tokenization

MFMs extend principles from LLMs, vision models, and autoregressive/diffusion generators into the music domain (Ma et al., 26 Aug 2024). The dominant pretraining strategy is self-supervised learning—especially masked modeling and contrastive learning. Masked modeling (e.g., HuBERT, w2v-BERT, MERT) is adapted for audio via masked token prediction on discrete or continuous features, compelling the model to learn contextual dependencies without labels (Won et al., 2023, Ma et al., 26 Aug 2024).

Tokenization strategies convert continuous music signals into discrete tokens compatible with transformer or state-space architectures. Notable approaches include:

• K-means clustering plus Residual Vector Quantization (RVQ) on normalized log-mel spectra, as in MERT.
• Random-projection quantization (inspired by BEST-RQ), enabling tokenization without a separate representation learning phase via

$$\tau = \arg\min_{i} \left|\|c_i\|_2 - \|R x\|_2\right|$$

where $R$ is a random projection matrix and $C$ the codebook (Won et al., 2023).

Model architectures comprise transformer encoders (BERT-style for mask prediction) and Conformer variants augmenting self-attention with convolutional modules for capturing both local and global structure. The Conformer backbone consistently outperforms standard transformers for both token-level (beat tracking) and sequence-level (music tagging) MIR tasks (Won et al., 2023). Hierarchical autoencoding (e.g., in SoniDo (Liao et al., 2 Nov 2024)) and VQ-VAE-style codecs are employed for compact discrete representations, especially in generative models (e.g., Jukebox, MusicGen).

2. Scaling, Temporal Resolution, and Model Versatility

Scalability is central to MFM performance. Training on datasets spanning 8k to 160k hours of audio demonstrates that, while modest datasets suffice for basic tasks, models trained on larger or more culturally diverse data generalize more robustly, especially to previously unseen musical forms (Won et al., 2023, Papaioannou et al., 20 Jun 2025). Model parameter counts at the upper hundreds of millions (e.g., 330M–660M) are linked to higher performance, with larger models typically more effective on non-Western and cross-cultural corpora—though diminishing returns and domain biases persist (Papaioannou et al., 20 Jun 2025).

Temporal resolution must be tuned to the inference domain. Coarse resolutions (e.g., 25 Hz) balance computational efficiency with adequate temporal sensitivity for MIR tasks such as beat tracking and chord recognition, while finer resolutions (50–75 Hz) may be superfluous for global or slower forms of analysis (Won et al., 2023). Temporal adaptation techniques, such as expanding input window size and reducing temporal frame rates during fine-tuning, enable efficient structure analysis on full-length songs without increased memory or runtime, addressing the limitations of fixed, short training windows (Zhang et al., 17 Jul 2025).

MFMs are evaluated via a battery of token-level (per-frame) and sequence-level (songwise) tasks:

Task Type	Examples	Typical Metrics
Token-level	Beat tracking, structure	F-measure, HR.5F, ACC
Sequence-level	Key detection, tagging	Accuracy, mAP, ROC-AUC

These are computed using shallow probes to isolate the pre-trained model’s representational quality (Won et al., 2023, Papaioannou et al., 20 Jun 2025).

3. Downstream Adaptation and Transfer, Including Parameter-Efficient Methods

MFMs are broadly transferable. Probing and full fine-tuning are common adaptation methodologies, but each is limited—probing may be suboptimal due to frozen representations, while fine-tuning is susceptible to overfitting and inefficiency (Ding et al., 28 Nov 2024). Parameter-Efficient Transfer Learning (PETL) methods, including:

• Adapter-based (inserting small adaptable modules between layers)
• Prompt-based (task-specific learned context vectors)
• Reparameterization-based (constraining parameter updates to low-rank additions)

offer an effective compromise, adapting only a small subset of parameters:

$$h' = h + \mathrm{Adapter}(h), \qquad x^* = [\mathrm{Prompt}; x], \qquad W' = W + \Delta W$$

These methods outperform both probing and fine-tuning on auto-tagging, and match full fine-tuning on key and tempo tasks with far less compute (Ding et al., 28 Nov 2024). However, in simple cases (e.g., key detection) small models trained from scratch may match or exceed adapted MFMs, indicating context-dependent value for large-scale pretraining.

For data-scare settings, hierarchical intermediate features extracted from MFMs, as in SoniDo, substantially improve performance on downstream tagging, transcription, source separation, and mixing, especially when limited labeled data are provided (Liao et al., 2 Nov 2024).

4. Evaluation of Representation, Generalization, and Music-Theoretic Content

Rigorous benchmarking indicates MFMs encode a spectrum of musical knowledge:

• Probing experiments using synthetic data test for explicit encoding of Western music theory concepts (tempo, key, intervals, chords, progressions). MFMs such as Jukebox and MusicGen exhibit robust and layer-dependent encodings of these concepts—decoder layers generally yield the most informative features (Wei et al., 1 Oct 2024).
• Performance on global MIR tasks (auto-tagging, key detection) is measured via ROC-AUC and mAP; micro- and macro-F1 are used in low-resource/few-shot regimes to assess generalization (Papaioannou et al., 20 Jun 2025).

Cross-cultural evaluation reveals Western-centric biases: while models perform well on Western datasets (FMA-medium, MagnaTagATune), performance drops on Greek, Turkish, and Indian classical corpora. Larger models are more robust, but training data diversity and extraction strategy are critical. In data-scarce, multi-label few-shot settings, MFMs generally outperform shallow baselines but exhibit only modest improvements in non-Western contexts (Papaioannou et al., 20 Jun 2025).

5. Applications, Extensions, and Multimodality

MFMs have demonstrated wide applicability:

• MIR tasks: beat/downbeat tracking, structure analysis (boundary and function prediction), chord recognition, instrument identification, music tagging (Won et al., 2023, Ru et al., 13 Aug 2025, Liao et al., 2 Nov 2024).
• Music generation: via autoregressive (MusicGen, Jukebox) and diffusion (ACE-Step) models, supporting advanced control (voice cloning, lyric editing, track remixing) and rapid, high-fidelity multi-minute synthesis (Gong et al., 28 May 2025).
• Cross-modal and retrieval-augmented applications: integrating text/audio (e.g., via lyric-aligned encoding), instructable generation, music–dance alignment, and music agents capable of question-answering and interaction (Ma et al., 26 Aug 2024, Liu et al., 27 Feb 2025).
• Education and production: adaptive tutoring, transcription, annotation, smart mixing, and DAW/plugin integration (Liao et al., 2 Nov 2024, Wei et al., 14 Sep 2024).

Multimodal FMs (e.g., CLAP, MuLan) and joint audio-language architectures underpin advances in music captioning, emotional analysis, and agents with structured musical reasoning (Ma et al., 26 Aug 2024, Li et al., 15 Sep 2024).

6. Current Limitations, Ethical Challenges, and Future Directions

Limitations of current MFMs include:

• Data scarcity and coverage: High-quality, multilingual, and multi-format datasets (audio, symbolic, annotated) remain limited, hindering robustness and generalization to non-Western traditions (Wei et al., 14 Sep 2024, Papaioannou et al., 20 Jun 2025).
• Cultural and domain biases: Models exhibit reduced accuracy on culturally distant repertoires and unexplored representations (e.g., cross-modal, graph-based encodings remain sparse).
• Interference and trade-off: Adding more modalities (e.g., vision, speech) or focusing on generative tasks can lead to interference and diminished performance for core music understanding (Li et al., 15 Sep 2024).
• Controllability and interpretability: While models deliver impressive outputs, explicit steering of musical parameters (melody, harmony, rhythm) remains a challenge, as does post-hoc interpretability (Ma et al., 26 Aug 2024).

Ethical concerns span copyright (risk of reproducing copyrighted content during training/generation), fairness (perpetuation of Western-centrism), personality rights (protection from voice cloning/deepfakes), and transparency (Ma et al., 26 Aug 2024). Concrete strategies—improved dataset curation, transparent documentation, watermarking, and regulatory frameworks—are indicated as priorities.

Future work is anticipated in:

• Multimodal and symbolic integration: Fusing audio, symbolic, textual, and visual modalities into unified representations (Ma et al., 26 Aug 2024).
• Long-sequence and hierarchical modeling: Development of more efficient architectures (sparse/hierarchical transformers, state-space models) to scale to long-form music.
• In-context learning, instruction tuning, agents: Advanced training strategies, chain-of-thought prompting, and real-time, interactive music agents with embedded reasoning and planning (Ma et al., 26 Aug 2024, Li et al., 15 Sep 2024).
• More culturally inclusive training regimes, and evaluation benchmarks spanning broader musical diversity and new music-theoretic constructs (Papaioannou et al., 20 Jun 2025).

7. Outlook and Resources

Open-source MFMs and benchmarks—such as SoniDo (Liao et al., 2 Nov 2024), ACE-Step (Gong et al., 28 May 2025), and the MuCUE comprehensive evaluation suite (Jiang et al., 2 Aug 2025)—accelerate reproducibility and downstream application. The increasing use of hierarchical features, parameter-efficient adaptation, and end-to-end multimodal training is establishing MFMs as the unifying substrate for next-generation music analysis, composition, retrieval, and creative AI.

The trajectory of MFMs encompasses ongoing advances in representation learning, cross-domain transfer, and human-AI collaboration, setting the stage for increasingly transparent, adaptable, and inclusive music technology.