117 citations · 129 across the 11 of their papers we have counts for
11 papers
FSD50K-Solo: Automated Curation of Single-Source Sound Events
Ningyuan Yang, Sile Yin, Li-Chia Yang +4
High-quality training datasets are essential for the performance of neural networks. However, the audio domain still lacks a large-scale, strongly-labeled, and single-source sound…
PAS-SE: Personalized Auxiliary-Sensor Speech Enhancement for Voice Pickup in Hearables
Mattes Ohlenbusch, Mikolaj Kegler, Marko Stamenovic
Speech enhancement for voice pickup in hearables aims to improve the user's voice by suppressing noise and interfering talkers, while maintaining own-voice quality. For single-chan…
CATSE: A Context-Aware Framework for Causal Target Sound Extraction
Shrishail Baligar, Mikolaj Kegler, Bryce Irvin +2
Target Sound Extraction (TSE) focuses on the problem of separating sources of interest, indicated by a user's cue, from the input mixture. Most existing solutions operate in an off…
Latent CLAP Loss for Better Foley Sound Synthesis
Tornike Karchkhadze, Hassan Salami Kavaki, Mohammad Rasool Izadi +5
Foley sound generation, the art of creating audio for multimedia, has recently seen notable advancements through text-conditioned latent diffusion models. These systems use multimo…
Two-Step Knowledge Distillation for Tiny Speech Enhancement
Rayan Daod Nathoo, Mikolaj Kegler, Marko Stamenovic
Tiny, causal models are crucial for embedded audio machine learning applications. Model compression can be achieved via distilling knowledge from a large teacher into a smaller stu…
CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment
Yuchen Liu, Li-Chia Yang, Alex Pawlicki +1
Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing. Traditional intrusive speech qua…
Self-Supervised Learning for Speech Enhancement through Synthesis
Bryce Irvin, Marko Stamenovic, Mikolaj Kegler +1
Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In…
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators
Marko Stamenovic, Nils L. Westhausen, Li-Chia Yang +2
We explore network sparsification strategies with the aim of compressing neural speech enhancement (SE) down to an optimal configuration for a new generation of low power microcont…
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Igor Fedorov, Marko Stamenovic, Carl Jensen +5
Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment in hear…
Machine Identification of High Impact Research through Text and Image Analysis
Marko Stamenovic, Jeibo Luo
The volume of academic paper submissions and publications is growing at an ever increasing rate. While this flood of research promises progress in various fields, the sheer volume…
Towards Cover Song Detection with Siamese Convolutional Neural Networks
Marko Stamenovic
A cover song, by definition, is a new performance or recording of a previously recorded, commercially released song. It may be by the original artist themselves or a different arti…