SLAP: A New Architecture for Joint Audio-Text Embeddings

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TLDR

SLAP is a new approach to multimodal learning that improves how audio and text are represented together. By removing the need for negative samples and using a self-supervised training method, it creates a more unified embedding space and outperforms traditional contrastive models on tasks like retrieval and classification. The result is more efficient, scalable, and accurate multimodal systems.

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The Context

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You may be familiar with multimodal embedding spaces: systems that map different kinds of data (images, video, audio, text) into a shared high-dimensional space.

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The idea is simple but powerful:

• An audio track goes through an audio encoder.
• A textual description goes through a text encoder.
• Both outputs are embedding vectors.

If trained properly, embeddings for related pairs (e.g. a song and its caption) sit close together, while unrelated ones are far apart.

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This enables:

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• Retrieval: type “upbeat jazz saxophone” and retrieve matching tracks.
• Recommendation: cluster songs with similar embeddings.
• Music captioning: generate descriptions based on nearest-neighbour captions.
• Conditional generation: guide music creation with text prompts.

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OpenAI’s CLIP pioneered this approach for images, and CLAP extended it to audio. Both rely on contrastive learning with positive (matching) and negative (non-matching) pairs.

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The Limitations

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Contrastive methods like CLIP/CLAP work well, but they have drawbacks:

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• Large batch sizes needed: small batches fail, while large ones require heavy compute.
• Modality gap: audio and text embeddings tend to form separate clusters rather than truly mixing, leading to strange “cone-like” structures in the embedding space.

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This means the shared space isn’t as efficiently leveraged as it could be.

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Enter SLAP

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SLAP (Siamese Language Audio Pre-Training) tackles these issues by eliminating the need for negative pairs.

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Why is that tricky?

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• If you only minimise the distance between matching pairs, the network can collapse into a trivial solution: always outputting the same embedding vector, regardless of input.
• Contrastive losses avoid this by comparing to negatives, but that introduces the modality gap and scaling issues.

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SLAP borrows an idea from image representation learning called Bootstrap Your Own Latent (BYOL).

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How It Works

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For both the audio and text branches, SLAP introduces a target network:

• Initially random, but updated over time as an exponential moving average of the encoder network’s weights.
• Effectively, the target network is a “lagging version” of the encoder.

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This ensures embeddings don’t collapse while avoiding the need for handcrafted negatives.

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SLAP optimises with two types of loss:

• Intermodal loss: audio vs. text across target/encoder pairs.
• Intramodal loss: each branch compared to its own target.

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A balance between the two is crucial, too much weight on either side leads to trivial or unstable solutions.

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Results

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The paper shows compelling improvements:

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• Embedding space: SLAP reduces the modality gap, with audio and text embeddings more naturally interwoven.
• Downstream tasks: genre classification, instrument recognition, tagging — SLAP is competitive with or outperforms CLAP and MusiCLaP (a strong baseline).
• Retrieval tasks: consistently strong, often better than contrastive approaches.

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In short, SLAP learns a more genuinely multimodal representation while sidestepping the inefficiencies of contrastive learning.

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Why It Matters

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SLAP is an important step toward more effective multimodal systems, particularly for music understanding and generation. By fixing structural issues in the embedding space, it opens the door to:

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• More accurate search and retrieval.
• Richer recommendation and tagging.
• Stronger foundations for generative models conditioned on text.

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It’s a reminder that even in a field dominated by scale, architectural innovations still matter.

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Reference

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Guinot, Julien, et al. “SLAP: Siamese Language-Audio Pretraining Without Negative Samples for Music Understanding.” arXiv preprint arXiv:2506.17815 (2025).

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