音频基础模型在音乐相似性中的事后解耦探索
Exploring Post-Hoc Disentanglement of Audio Foundation Models for Music Similarity
Lucas de Souza Brandão, Gabriel da Costa Merlin, Diego Furtado Silva
AI 速读 基于摘要
该研究系统考察如何在不微调冻结预训练音频基础模型的前提下，用条件相似性网络（CSN）的掩码门控实现属性特定的嵌入子空间解耦。
cs.SD · cs.LG
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Audio foundation models like CLAP, MuQ, and MERT excel at capturing general musical similarity. On the other hand, their entangled latent spaces limit interpretability and fine-grained attribute control. Conditional Similarity Networks (CSN) address this by pairing a backbone representation model with learned masks that gate the embedding space into attribute-specific subspaces. However, existing methods train the backbone from scratch, rather than leveraging pretrained foundation models. We instead present a systematic study of post-hoc disentanglement, adapting CSN to gate frozen pretrained embeddings. We evaluate adding trainable parameters to adapt the embedding under different masking and pooling regimes across two datasets. Our findings show that minimal adaptation capacity is sufficient for post-hoc gating to improve both attribute-specific disentanglement and overall retrieval performance, while pooling, masking, and depth choices yield limited additional gains.