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Found 6 papers, 1 papers with code
Why does music source separation benefit from cacophony?
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs.
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter.
Generation or Replication: Auscultating Audio Latent Diffusion Models
The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how we work with audio.
Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT
In this paper, we propose a self-supervised learning framework for music source separation inspired by the HuBERT speech representation model.
Cold Diffusion for Speech Enhancement
Diffusion models have recently shown promising results for difficult enhancement tasks such as the conditional and unconditional restoration of natural images and audio signals.
Late Audio-Visual Fusion for In-The-Wild Speaker Diarization
Speaker diarization is well studied for constrained audios but little explored for challenging in-the-wild videos, which have more speakers, shorter utterances, and inconsistent on-screen speakers.
