20 citations · 28 across the 5 of their papers we have counts for
9 papers
BEANS: The Benchmark of Animal Sounds
Masato Hagiwara, Benjamin Hoffman, Jen-Yu Liu +3
The use of machine learning (ML) based techniques has become increasingly popular in the field of bioacoustics over the last years. Fundamental requirements for the successful appl…
Modeling Animal Vocalizations through Synthesizers
Masato Hagiwara, Maddie Cusimano, Jen-Yu Liu
Modeling real-world sound is a fundamental problem in the creative use of machine learning and many other fields, including human speech processing and bioacoustics. Transformer-ba…
KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms
Chien-Feng Liao, Jen-Yu Liu, Yi-Hsuan Yang
In this paper, we propose a novel neural network model called KaraSinger for a less-studied singing voice synthesis (SVS) task named score-free SVS, in which the prosody and melody…
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh +1
To apply neural sequence models such as the Transformers to music generation tasks, one has to represent a piece of music by a sequence of tokens drawn from a finite set of pre-def…
Score and Lyrics-Free Singing Voice Generation
Jen-Yu Liu, Yu-Hua Chen, Yin-Cheng Yeh +1
Generative models for singing voice have been mostly concerned with the task of ``singing voice synthesis,'' i.e., to produce singing voice waveforms given musical scores and text…
Dilated Convolution with Dilated GRU for Music Source Separation
Jen-Yu Liu, Yi-Hsuan Yang
Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they c…