3 citations · 4 across the 8 of their papers we have counts for
9 papers
Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers
Faris Chaudhry, Anthea Monod, Keisuke Yano
Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric conditioning, the convergence rate…
Hyperbolic Distance-Based Speech Separation
Darius Petermann, Minje Kim
In this work, we explore the task of hierarchical distance-based speech separation defined on a hyperbolic manifold. Based on the recent advent of audio-related tasks performed in…
Native Multi-Band Audio Coding within Hyper-Autoencoded Reconstruction Propagation Networks
Darius Petermann, Inseon Jang, Minje Kim
Spectral sub-bands do not portray the same perceptual relevance. In audio coding, it is therefore desirable to have independent control over each of the constituent bands so that b…
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
Darius Petermann, Gordon Wichern, Aswin Shanmugam Subramanian +2
Emulating the human ability to solve the cocktail party problem, i.e., focus on a source of interest in a complex acoustic scene, is a long standing goal of audio source separation…
Hyperbolic Audio Source Separation
Darius Petermann, Gordon Wichern, Aswin Subramanian +1
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…
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
Darius Petermann, Minje Kim
With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While e…
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
Darius Petermann, Gordon Wichern, Zhong-Qiu Wang +1
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mai…
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding
Darius Petermann, Seungkwon Beack, Minje Kim
An autoencoder-based codec employs quantization to turn its bottleneck layer activation into bitstrings, a process that hinders information flow between the encoder and decoder par…
Deep Learning Based Source Separation Applied To Choir Ensembles
Darius Petermann, Pritish Chandna, Helena Cuesta +2
Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced setting for choir ens…