Learning Interpretable Features in Audio Latent Spaces via Sparse Autoencoders

Nathan Paek, Yongyi Zang, Qihui Yang +1

While sparse autoencoders (SAEs) successfully extract interpretable features from language models, applying them to audio generation faces unique challenges: audio's dense nature r…