Unsupervised Vocal Dereverberation with Diffusion-based Generative ModelsView Publication
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb, and artificial reverb. Artificial reverb has a wider diversity than natural reverb due to its various parameter setups and reverberation types. However, recent supervised dereverberation methods may fail because they rely on sufficiently diverse and numerous pairs of reverberant observations and retrieved data for training in order to be generalizable to unseen observations during inference. To resolve these problems, we propose an unsupervised method that can remove a general kind of artificial reverb for music without requiring pairs of data for training. The proposed method is based on diffusion models, where it initializes the unknown reverberation operator with a conventional signal processing technique and simultaneously refines the estimate with the help of diffusion models. We show through objective and perceptual evaluations that our method outperforms the current leading vocal dereverberation benchmarks.
Related PublicationsView All
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
Kazuki Shimada, Archontis Politis*, Parthasaarathy Sudarsanam*, Daniel Krause*, Kengo Uchida, Sharath Adavanne*, Aapo Hakala*, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Tuomas Virtanen*, Yuki MitsufujiWhile direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded […]
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida, Wei-Hsiang Liao, Yuki MitsufujiTaking long-term spectral and temporal dependencies into account is essential for automatic piano transcriptio […]