AI RESEARCH
SAHMM-VAE: A Source-Wise Adaptive Hidden Markov Prior Variational Autoencoder for Unsupervised Blind Source Separation
arXiv CS.LG
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ArXi:2603.25776v1 Announce Type: cross We propose SAHMM-VAE, a source-wise adaptive Hidden Marko prior variational autoencoder for unsupervised blind source separation. Instead of treating the latent prior as a single generic regularizer, the proposed framework assigns each latent dimension its own adaptive regime-switching prior, so that different latent dimensions are pulled toward different source-specific temporal organizations during