293 citations · 570 across the 10 of their papers we have counts for
10 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…
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…
Remixing Music with Visual Conditioning
Li-Chia Yang, Alexander Lerch
We propose a visually conditioned music remixing system by incorporating deep visual and audio models. The method is based on a state of the art audio-visual source separation mode…
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…
Neural Wavetable: a playable wavetable synthesizer using neural networks
Lamtharn Hantrakul, Li-Chia Yang
We present Neural Wavetable, a proof-of-concept wavetable synthesizer that uses neural networks to generate playable wavetables. The system can produce new, distinct waveforms thro…
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang +1
Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed…
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu +2
Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by e…
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Li-Chia Yang, Szu-Yu Chou, Yi-Hsuan Yang
Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural netwo…