Search Results for author:
Found 11 papers, 6 papers with code
ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Sounds
Traditionally, bioacoustics has relied on spectrograms and continuous, per-frame audio representations for the analysis of animal sounds, also serving as input to machine learning models.
KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms
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 are spontaneously decided by machine.
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types.
Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization
Audio examples, as well as the code for implementing our model, will be publicly available online upon paper publication.
Score and Lyrics-Free Singing Voice Generation
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 lyrics.
Dilated Convolution with Dilated GRU for Music Source Separation
To reach information at remote locations, we propose to combine dilated convolution with a modified version of gated recurrent units (GRU) called the `Dilated GRU' to form a block.
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
Can we make a famous rap singer like Eminem sing whatever our favorite song?
Weakly-supervised Visual Instrument-playing Action Detection in Videos
Instrument playing is among the most common scenes in music-related videos, which represent nowadays one of the largest sources of online videos.
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant applications in the music industry.
Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals
Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems.
