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Found 8 papers, 1 papers with code
Hyperbolic Distance-Based Speech Separation
In this work, we explore the task of hierarchical distance-based speech separation defined on a hyperbolic manifold.
Native Multi-Band Audio Coding within Hyper-Autoencoded Reconstruction Propagation Networks
Spectral sub-bands do not portray the same perceptual relevance.
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
In this paper, we focus on the cocktail fork problem, which takes a three-pronged approach to source separation by separating an audio mixture such as a movie soundtrack or podcast into the three broad categories of speech, music, and sound effects (SFX - understood to include ambient noise and natural sound events).
Hyperbolic Audio Source Separation
We introduce a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features.
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem.
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research.
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding
The assumption is that, in a mirrored autoencoder topology, a decoder layer reconstructs the intermediate feature representation of its corresponding encoder layer.
Deep Learning Based Source Separation Applied To Choir Ensembles
However, most of the research has been focused on a typical case which consists in separating vocal, percussion and bass sources from a mixture, each of which has a distinct spectral structure.
