Search Results for author:
Found 15 papers, 1 papers with code
Type I and Type II Bayesian Methods for Sparse Signal Recovery using Scale Mixtures
In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR).
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers.
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem.
From Speech-to-Speech Translation to Automatic Dubbing
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing.
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on
Speech Enhancement
on CHiME-3
PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
Neural network applications generally benefit from larger-sized models, but for current speech enhancement models, larger scale networks often suffer from decreased robustness to the variety of real-world use cases beyond what is encountered in training data.
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output.
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model's performance.
Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size and complexity.
Improved singing voice separation with chromagram-based pitch-aware remixing
Singing voice separation aims to separate music into vocals and accompaniment components.
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets
In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech.
Semi-supervised Time Domain Target Speaker Extraction with Attention
In this work, we propose Exformer, a time-domain architecture for target speaker extraction.
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement.
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure
The algorithm runs in real-time on 10-ms frames with a 40 ms of look-ahead.
