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Found 9 papers, 4 papers with code
CATSE: A Context-Aware Framework for Causal Target Sound Extraction
First, we explore the utility of context by providing the TSE model with oracle information about what sound classes make up the input mixture, where the objective of the model is to extract one or more sources of interest indicated by the user.
Latent CLAP Loss for Better Foley Sound Synthesis
We introduce a new loss term to enhance Foley sound generation in AudioLDM without post-filtering.
Two-Step Knowledge Distillation for Tiny Speech Enhancement
Tiny, causal models are crucial for embedded audio machine learning applications.
CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment
Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing.
Self-Supervised Learning for Speech Enhancement through Synthesis
Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction.
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators
Using weight pruning, we show that we are able to compress an already compact model's memory footprint by a factor of 42x from 3. 7MB to 87kB while only losing 0. 1 dB SDR in performance.
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs).
Machine Identification of High Impact Research through Text and Image Analysis
This new dataset allows us to expand on current work in the field by generalizing across time and academic domain.
Towards Cover Song Detection with Siamese Convolutional Neural Networks
A cover song, by definition, is a new performance or recording of a previously recorded, commercially released song.
