research
∙
03/14/2023
Native Multi-Band Audio Coding within Hyper-Autoencoded Reconstruction Propagation Networks
Spectral sub-bands do not portray the same perceptual relevance. In audi...
share
research
∙
11/14/2022
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement
With the advances in deep learning, speech enhancement systems benefited...
research
∙
11/04/2022
Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding
Low and ultra-low-bitrate neural speech coding achieves unprecedented co...
research
∙
03/22/2022
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content
In the stereo-to-multichannel upmixing problem for music, one of the mai...
research
∙
02/15/2022
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
With the recent advancements of data driven approaches using deep neural...
research
∙
11/17/2021
BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement
In this paper, we present a blockwise optimization method for masking-ba...
research
∙
07/28/2021
Neural Remixer: Learning to Remix Music with Interactive Control
The task of manipulating the level and/or effects of individual instrume...
research
∙
07/22/2021
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding
An autoencoder-based codec employs quantization to turn its bottleneck l...
research
∙
05/08/2021
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
In realistic speech enhancement settings for end-user devices, we often ...
research
∙
05/08/2021
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection
This paper presents a novel zero-shot learning approach towards personal...
research
∙
04/05/2021
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Training personalized speech enhancement models is innately a no-shot le...
research
∙
04/05/2021
Self-Supervised Learning for Personalized Speech Enhancement
Speech enhancement systems can show improved performance by adapting the...
research
∙
03/27/2021
Scalable and Efficient Neural Speech Coding
This work presents a scalable and efficient neural waveform codec (NWC) ...
research
∙
12/31/2020
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding
Conventional audio coding technologies commonly leverage human perceptio...
research
∙
11/06/2020
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement
This work explores how self-supervised learning can be universally used ...
research
∙
05/16/2020
Sparse Mixture of Local Experts for Efficient Speech Enhancement
In this paper, we investigate a deep learning approach for speech denois...
research
∙
02/14/2020
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Speech enhancement tasks have seen significant improvements with the adv...
research
∙
02/13/2020
Efficient And Scalable Neural Residual Waveform Coding With Collaborative Quantization
Scalability and efficiency are desired in neural speech codecs, which su...
research
∙
02/12/2020
Deep Autotuner: a Pitch Correcting Network for Singing Performances
We introduce a data-driven approach to automatic pitch correction of sol...
research
∙
08/26/2019
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
We propose an iteration-free source separation algorithm based on Winner...
research
∙
08/23/2019
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation
This paper proposes a Bitwise Gated Recurrent Unit (BGRU) network for th...
research
∙
08/18/2019
A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
In speech enhancement, an end-to-end deep neural network converts a nois...
research
∙
08/18/2019
Efficient Context Aggregation for End-to-End Speech Enhancement Using a Densely Connected Convolutional and Recurrent Network
In speech enhancement, an end-to-end deep neural network converts a nois...
research
∙
06/18/2019
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding
Speech codecs learn compact representations of speech signals to facilit...
research
∙
05/03/2019
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
Understanding the bottlenecks in implementing stochastic gradient descen...
research
∙
02/27/2019
Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation
The convergence of HPC and data-intensive methodologies provide a promis...
research
∙
02/15/2019
AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices
In this paper, we propose a hierarchical deep reinforcement learning (DR...
research
∙
02/03/2019
Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances
We describe a machine-learning approach to pitch correcting a solo singi...
research
∙
05/07/2018
A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music
In this paper, we present a machine-learning approach to pitch correctio...
research
∙
01/29/2018
On Psychoacoustically Weighted Cost Functions Towards Resource-Efficient Deep Neural Networks for Speech Denoising
We present a psychoacoustically enhanced cost function to balance networ...
research
∙
12/19/2017
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
We present DeepPicar, a low-cost deep neural network based autonomous ca...
research
∙
08/22/2017
Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics
This paper proposes an efficient bitwise solution to the single-channel ...
research
∙
01/22/2016
Bitwise Neural Networks
Based on the assumption that there exists a neural network that efficien...
research
∙
02/13/2015
