Search Results for author: Yi Luo

Found 47 papers, 17 papers with code

High Fidelity Speech Enhancement with Band-split RNN

no code implementations1 Dec 2022 Jianwei Yu, Yi Luo, Hangting Chen, Rongzhi Gu, Chao Weng

This report presents the development of our speech enhancement system, which includes the use of a recently proposed music separation model, the band-split recurrent neural network (BSRNN), and a MetricGAN-based training objective to improve non-differentiable quality metrics such as perceptual evaluation of speech quality (PESQ) score.

Speech Enhancement

Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models

1 code implementation19 Nov 2022 Yi Luo, Guiduo Duan, Guangchun Luo, Aiguo Chen

For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes according to node features propagated along the graph structure.

Node Classification

3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography

no code implementations11 Oct 2022 Lin Wang, Xiufen Ye, Donghao Zhang, Wanji He, Lie Ju, Yi Luo, Huan Luo, Xin Wang, Wei Feng, Kaimin Song, Xin Zhao, ZongYuan Ge

In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.

Binarization Image Matting

Music Source Separation with Band-split RNN

no code implementations30 Sep 2022 Yi Luo, Jianwei Yu

The performance of music source separation (MSS) models has been greatly improved in recent years thanks to the development of novel neural network architectures and training pipelines.

Ranked #2 on Music Source Separation on MUSDB18 (using extra training data)

Music Source Separation

Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

no code implementations30 Aug 2022 Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu, Yichen Wu, Aydogan Ozcan

To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window.

Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network

no code implementations13 Aug 2022 Jingxi Li, Bijie Bai, Yi Luo, Aydogan Ozcan

We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued linear transformations between an input and output field-of-view, each with N_i and N_o pixels, respectively.

FRA-RIR: Fast Random Approximation of the Image-source Method

1 code implementation8 Aug 2022 Yi Luo, Jianwei Yu

The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments.

Denoising Speech Denoising

All-optical image classification through unknown random diffusers using a single-pixel diffractive network

no code implementations8 Aug 2022 Yi Luo, Bijie Bai, Yuhang Li, Ege Cetintas, Aydogan Ozcan

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.

Autonomous Driving Image Classification

Super-resolution image display using diffractive decoders

no code implementations15 Jun 2022 Cagatay Isil, Deniz Mengu, Yifan Zhao, Anika Tabassum, Jingxi Li, Yi Luo, Mona Jarrahi, Aydogan Ozcan

We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.


To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects

no code implementations26 May 2022 Bijie Bai, Yi Luo, Tianyi Gan, Jingtian Hu, Yuhang Li, Yifan Zhao, Deniz Mengu, Mona Jarrahi, Aydogan Ozcan

Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.

Privacy Preserving

Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers

no code implementations1 May 2022 Yuhang Li, Yi Luo, Bijie Bai, Aydogan Ozcan

During its training, random diffusers with a range of correlation lengths were used to improve the diffractive network's generalization performance.

Autonomous Driving Image Reconstruction

Inferring from References with Differences for Semi-Supervised Node Classification on Graphs

1 code implementation Mathematics 2022 Yi Luo, Guangchun Luo, Ke Yan, Aiguo Chen

Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years.

Node Classification

Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization

1 code implementation CVPR 2022 Wei Dong, Junsheng Wu, Yi Luo, ZongYuan Ge, Peng Wang

In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing.

Node Classification Representation Learning

A Time-domain Real-valued Generalized Wiener Filter for Multi-channel Neural Separation Systems

1 code implementation7 Dec 2021 Yi Luo

Frequency-domain beamformers have been successful in a wide range of multi-channel neural separation systems in the past years.

Speech Separation

Cascadable all-optical NAND gates using diffractive networks

no code implementations2 Nov 2021 Yi Luo, Deniz Mengu, Aydogan Ozcan

Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers to all-optically perform NAND operation using the diffraction of light, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another.

Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

2 code implementations16 Jun 2021 Yi Luo, Aiguo Chen, Ke Yan, Ling Tian

Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.

Node Classification

Dynamic imaging and characterization of volatile aerosols in e-cigarette emissions using deep learning-based holographic microscopy

no code implementations31 Mar 2021 Yi Luo, Yichen Wu, Liqiao Li, Yuening Guo, Ege Cetintas, Yifang Zhu, Aydogan Ozcan

To evaluate the effects of e-liquid composition on aerosol dynamics, we measured the volatility of the particles generated by flavorless, nicotine-free e-liquids with various PG/VG volumetric ratios, revealing a negative correlation between the particles' volatility and the volumetric ratio of VG in the e-liquid.

Memory-Associated Differential Learning

2 code implementations10 Feb 2021 Yi Luo, Aiguo Chen, Bei Hui, Ke Yan

Conventional Supervised Learning approaches focus on the mapping from input features to output labels.

Link Prediction

Group Communication with Context Codec for Lightweight Source Separation

1 code implementation14 Dec 2020 Yi Luo, Cong Han, Nima Mesgarani

A context codec module, containing a context encoder and a context decoder, is designed as a learnable downsampling and upsampling module to decrease the length of a sequential feature processed by the separation module.

Speech Enhancement Speech Separation

Drone LAMS: A Drone-based Face Detection Dataset with Large Angles and Many Scenarios

no code implementations16 Nov 2020 Yi Luo, Siyi Chen, X. -G. Ma

This work presented a new drone-based face detection dataset Drone LAMS in order to solve issues of low performance of drone-based face detection in scenarios such as large angles which was a predominant working condition when a drone flies high.

Face Detection

Integrated Gallium Nitride Nonlinear Photonics

no code implementations30 Oct 2020 Yanzhen Zheng, Changzheng Sun, Bing Xiong, Lai Wang, Zhibiao Hao, Jian Wang, Yanjun Han, Hongtao Li, Jiadong Yu, Yi Luo

Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications.

Optics Applied Physics

An End-to-end Architecture of Online Multi-channel Speech Separation

no code implementations7 Sep 2020 Jian Wu, Zhuo Chen, Jinyu Li, Takuya Yoshioka, Zhili Tan, Ed Lin, Yi Luo, Lei Xie

Previously, we introduced a sys-tem, calledunmixing, fixed-beamformerandextraction(UFE), that was shown to be effective in addressing the speech over-lap problem in conversation transcription.

speech-recognition Speech Recognition +1

Deep learning-based holographic polarization microscopy

no code implementations1 Jul 2020 Tairan Liu, Kevin de Haan, Bijie Bai, Yair Rivenson, Yi Luo, Hongda Wang, David Karalli, Hongxiang Fu, Yibo Zhang, John FitzGerald, Aydogan Ozcan

Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution.

Medical Diagnosis

Terahertz Pulse Shaping Using Diffractive Surfaces

no code implementations30 Jun 2020 Muhammed Veli, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Jingxi Li, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan

Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.

Transfer Learning

Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks

no code implementations15 May 2020 Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan

3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.

General Classification

Separating Varying Numbers of Sources with Auxiliary Autoencoding Loss

no code implementations27 Mar 2020 Yi Luo, Nima Mesgarani

Many recent source separation systems are designed to separate a fixed number of sources out of a mixture.

Continuous speech separation: dataset and analysis

1 code implementation30 Jan 2020 Zhuo Chen, Takuya Yoshioka, Liang Lu, Tianyan Zhou, Zhong Meng, Yi Luo, Jian Wu, Xiong Xiao, Jinyu Li

In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a \textit{continuous} audio stream that contains multiple utterances that are \emph{partially} overlapped by a varying degree.

Automatic Speech Recognition speech-recognition +1

End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation

2 code implementations30 Oct 2019 Yi Luo, Zhuo Chen, Nima Mesgarani, Takuya Yoshioka

An important problem in ad-hoc microphone speech separation is how to guarantee the robustness of a system with respect to the locations and numbers of microphones.

Speech Separation

Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation

7 code implementations14 Oct 2019 Yi Luo, Zhuo Chen, Takuya Yoshioka

Recent studies in deep learning-based speech separation have proven the superiority of time-domain approaches to conventional time-frequency-based methods.

Speech Separation

Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks

no code implementations14 Sep 2019 Yi Luo, Deniz Mengu, Nezih T. Yardimci, Yair Rivenson, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan

We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning.

Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy

no code implementations8 Jun 2019 Jingxi Li, Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan

Similar to ensemble methods practiced in machine learning, we also independently-optimized multiple differential diffractive networks that optically project their light onto a common detector plane, and achieved testing accuracies of 98. 59%, 91. 06% and 51. 44% for MNIST, Fashion-MNIST and grayscale CIFAR-10, respectively.

BIG-bench Machine Learning General Classification

Demand Prediction for Electric Vehicle Sharing

no code implementations10 Mar 2019 Man Luo, Hongkai Wen, Yi Luo, Bowen Du, Konstantin Klemmer, Hong-Ming Zhu

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe.

Decision Making

Response to Comment on "All-optical machine learning using diffractive deep neural networks"

no code implementations10 Oct 2018 Deniz Mengu, Yi Luo, Yair Rivenson, Xing Lin, Muhammed Veli, Aydogan Ozcan

In their Comment, Wei et al. (arXiv:1809. 08360v1 [cs. LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the system due to linearity and passivity.

BIG-bench Machine Learning

Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks

no code implementations3 Oct 2018 Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan

Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network.

BIG-bench Machine Learning General Classification

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation

17 code implementations20 Sep 2018 Yi Luo, Nima Mesgarani

The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.

Multi-task Audio Source Seperation Music Source Separation +3

Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network

1 code implementation ISCA Interspeech 2018 Yi Luo, Nima Mesgarani

We investigate the recently proposed Time-domain Audio Sep-aration Network (TasNet) in the task of real-time single-channel speech dereverberation.

Denoising Speech Dereverberation +1

TasNet: time-domain audio separation network for real-time, single-channel speech separation

3 code implementations1 Nov 2017 Yi Luo, Nima Mesgarani

We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs.

Speech Separation

Point Set Registration With Global-Local Correspondence and Transformation Estimation

no code implementations ICCV 2017 Su Zhang, Yang Yang, Kun Yang, Yi Luo, Sim-Heng Ong

We present a new point set registration method with global-local correspondence and transformation estimation (GL-CATE).

Speaker-independent Speech Separation with Deep Attractor Network

no code implementations12 Jul 2017 Yi Luo, Zhuo Chen, Nima Mesgarani

A reference point attractor is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space.

Speech Separation

Deep attractor network for single-microphone speaker separation

1 code implementation27 Nov 2016 Zhuo Chen, Yi Luo, Nima Mesgarani

We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source.

Speaker Separation Speech Separation

Deep Clustering and Conventional Networks for Music Separation: Stronger Together

no code implementations18 Nov 2016 Yi Luo, Zhuo Chen, John R. Hershey, Jonathan Le Roux, Nima Mesgarani

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks.

Deep Clustering Multi-Task Learning +2

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