Search Results for author:
Found 6 papers, 3 papers with code
Learning Semantic Information from Raw Audio Signal Using Both Contextual and Phonetic Representations
We propose a framework to learn semantics from raw audio signals using two types of representations, encoding contextual and phonetic information respectively.
EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning
We also introduce a new training objective called masked codec modeling that improves acoustic awareness of the pretrained language model.
Ranked #1 on
Audio captioning
on AudioCaps
Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates
Specifically, to insert a new foreground object represented by a set of multi-view images into a background radiance field, we use a text-to-image diffusion model to learn and generate combined images that fuse the object of interest into the given background across views.
PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS
Previous pitch-controllable text-to-speech (TTS) models rely on directly modeling fundamental frequency, leading to low variance in synthesized speech.
PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network.
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds
With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e. g., object recognition, semantic segmentation.
