Search Results for author:
Found 12 papers, 6 papers with code
SongCreator: Lyrics-based Universal Song Generation
While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals and accompaniment given lyrics remains a significant challenge, hindering the application of music generation models in the real world.
Foundation Models for Music: A Survey
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music.
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time.
Ranked #7 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
We propose a new bilateral denoising diffusion model (BDDM) that parameterizes both the forward and reverse processes with a schedule network and a score network, which can train with a novel bilateral modeling objective.
Ranked #1 on
Speech Synthesis
on LJSpeech
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations
Learning generalizable speech representations for unseen samples in different domains has been a challenge with ever increasing importance to date.
Bilateral Denoising Diffusion Models
In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples.
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition
End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization.
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect
We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference.
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation
One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throughout all layers.
Ranked #8 on
Speech Separation
on WSJ0-3mix
Contrastive Separative Coding for Self-supervised Representation Learning
To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC).
Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks
Recent research on the time-domain audio separation networks (TasNets) has brought great success to speech separation.
Ranked #15 on
Speech Separation
on WSJ0-2mix
Mixup-breakdown: a consistency training method for improving generalization of speech separation models
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditions.
