Search Results for author:
Found 20 papers, 8 papers with code
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels.
Ranked #1 on
Direction of Arrival Estimation
on SOFA
(using extra training data)
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks.
Scene-Aware Audio Rendering via Deep Acoustic Analysis
We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models.
Sound Graphics Multimedia Audio and Speech Processing
Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).
Fast 3D Acoustic Scattering via Discrete Laplacian Based Implicit Function Encoders
Acoustic properties of objects corresponding to scattering characteristics are frequently used for 3D audio content creation, environmental acoustic effects, localization and acoustic scene analysis, etc.
Scene-aware Far-field Automatic Speech Recognition
We use a deep learning-based estimator to non-intrusively compute the sub-band reverberation time of an environment from its speech samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Our stateful implementation for online separation leads to a minor drop in performance compared to the offline model; 0. 8dB for monaural inputs and 0. 3dB for binaural inputs while reaching a real-time factor of 0. 65.
Improving Reverberant Speech Separation with Multi-stage Training and Curriculum Learning
We present a novel approach that improves the performance of reverberant speech separation.
FAST-RIR: Fast neural diffuse room impulse response generator
We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes
We show that the acoustic metrics of the IRs predicted from our MESH2IR match the ground truth with less than 10% error.
Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation
We present a novel approach to improve the performance of learning-based speech dereverberation using accurate synthetic datasets.
Exploring Data Redundancy in Real-world Image Classification through Data Selection
Deep learning models often require large amounts of data for training, leading to increased costs.
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting
The core idea is to combine the powerful image generation capability of the 2D diffusion model and the texture alignment ability of the repainting strategy for generating high-quality multi-view images with consistency.
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs.
Ranked #52 on
Visual Question Answering
on MM-Vet
LLMBind: A Unified Modality-Task Integration Framework
In the multi-modal domain, the dependence of various models on specific input formats leads to user confusion and hinders progress.
Envision3D: One Image to 3D with Anchor Views Interpolation
To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation.
VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing
We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre.
