Research

LINE positions the AI business as one of our strategic targets and are actively expanding our business. With the aim of accelerating the research and development of AI technology and the development of businesses that utilize it, the teams responsible for data infrastructure development, data analysis, machine learning, AI technology development and basic research are consolidated into a single organization called “Data Science and Engineering Center.”
This strengthens cooperation between each team and speeds up the cycle of research, development and commercialization. In addition to creating new AI-related services and functions by maximizing the use of large scale data from across businesses and areas of responsibility, we are also focusing on further improving the user experience of various services.
Our specific research areas include machine learning, speech processing, language processing, and image processing. Regarding research themes, we have a system for conducting joint research in collaboration with many universities in Japan. In the future, we are also considering collaboration with overseas research institutes.
In addition to providing a relaxing office environment where you can immerse yourself in research and development, we frequently invite leading experts in the AI field from outside of the company to our events, so there is plenty of stimulation.
If you have a strong desire to do research and want to dive into a stimulating environment, we invite you to join us!

Publications

HUMAN-COMPUTER INTERACTION Conference

Continuous and Gradual Style Changes of Graphic Designs with Generative Model

  • M. Ueno and S. Satoh
  • IUI2021, 2021/4

MACHINE LEARNING SECURITY & PRIVACY Conference

P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

  • S. Takagi, T. Takahashi, Y. Cao and M. Yoshikawa
  • ICDE2021, 2021/4

SPEECH PROCESSING Conference

Sound Event Localization and Detection using a Recurrent Convolutional Neural Network and Gated Linear Unit

  • T. Komatsu, M. Togami, T. Takahashi
  • EUSIPCO 2020, 2021/1

SPEECH PROCESSING Conference

Robust Acoustic Scene Classification to Multiple Devices Using Maximum Classifier Discrepancy and Knowledge Distillation

  • S. Takeyama, T. Komatsu, K. Miyazaki, M. Togami, S. Ono
  • EUSIPCO 2020, 2021/1

MACHINE LEARNING Workshop

Disentangling Clustered Representations of Variational Autoencoders for Generating Diverse Samples

  • T. Takahashi, T. Komatsu and K. Yamada
  • LDRC at IJCAI2020, 2021/1

SPEECH PROCESSING Conference

Over-determined Speech Source Separation and Dereverberation

  • M. Togami and R. Scheibler
  • APSIPA2020, 2020/12

SPEECH PROCESSING Conference

INTEGRATION OF SEMI-BLIND SPEECH SOURCE SEPARATION AND VOICE ACTIVITY DETECTION FOR FLEXIBLE SPOKEN DIALOGUE

  • M. Wake, M. Togami, K. Yoshii, and T. Kawahara
  • APSIPA2020, 2020/12

SPEECH PROCESSING Conference

Computer-Resource-Aware Deep Speech Separation with a Run-Time-Specified Number of BLSTM Layers

  • M. Togami, Y. Masuyama, T. Komatsu, K. Yoshii, T. Kawahara
  • APSIPA2020, 2020/12

SPEECH PROCESSING Workshop

Conformer-based sound event detection with semi-supervised learning and data augmentation

  • K. Miyazaki, T. Komatsu, T. Hayashi, S. Watanabe, T. Toda, and K. Takeda
  • DCASE2020, 2020/11

SPEECH PROCESSING Conference

Generalized Minimal Distortion Principle for Blind Source Separation

  • R. Scheibler
  • INTERSPEECH2020, 2020/10

SPEECH PROCESSING Conference

Sparseness-Aware DOA Estimation with Majorization Minimization

  • M. Togami and R. Scheibler
  • INTERSPEECH2020, 2020/10

SPEECH PROCESSING Conference

Mentoring-Reverse Mentoring for Unsupervised Multi-channel Speech Source Separation

  • Y. Nakagome, M. Togami, T. Ogawa and T. Kobayashi
  • INTERSPEECH2020, 2020/10

MACHINE LEARNING SECURITY & PRIVACY Workshop

Locally Private Distributed Reinforcement Learning

  • H. Ono and T. Takahashi
  • FL-ICML at ICML2020, 2020/7

COMPUTER VISION Conference

Neural Implicit Embedding for Point Cloud Analysis

  • K. Fujiwara and T. Hashimoto
  • CVPR2020, 2020/6

SPEECH PROCESSING Conference

UNSUPERVISED TRAINING FOR DEEP SPEECH SOURCE SEPARATION WITH KULLBACK-LEIBLER DIVERGENCE BASED PROBABILISTIC LOSS FUNCTION

  • M. Togami, Y. Masuyama, T. Komatsu, and Y. Nakagome
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

DEEP SPEECH EXTRACTION WITH TIME-VARYING SPATIAL FILTERING GUIDED BY DESIRED DIRECTION ATTRACTOR

  • Y. Nakagome, M. Togami, T. Ogawa, and T. Kobayashi
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

CONSISTENCY-AWARE MULTI-CHANNEL SPEECH ENHANCEMENT USING DEEP NEURAL NETWORKS

  • Y. Masuyama, M. Togami, and T. Komatsu
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

SCENE-DEPENDENT ACOUSTIC EVENT DETECTION WITH SCENE CONDITIONING AND FAKE-SCENE-CONDITIONED LOSS

  • T. Komatsu, K. Imoto, M. Togami
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

WEAKLY-SUPERVISED SOUND EVENT DETECTION WITH SELF-ATTENTION

  • K. Miyazaki, T. Komatsu, T. Hayashi, S. Watanabe, T. Toda, K. Takeda
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

IMPROVING LPCNET-BASED TEXT-TO-SPEECH WITH LINEAR PREDICTION-STRUCTURED MIXTURE DENSITY NETWORK

  • M.-J. Hwang, E. Song, R. Yamamoto, F. Soong, H.-G. Kang
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

ESPNET-TTS: UNIFIED, REPRODUCIBLE, AND INTEGRATABLE OPEN SOURCE END-TO-END TEXT-TO-SPEECH TOOLKIT

  • T. Hayashi, R. Yamamoto, K. Inoue, T. Yoshimura, S. Watanabe, T. Toda, K. Takeda, Y. Zhang, X. Tan
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

SEMI-SUPERVISED SPEAKER ADAPTATION FOR END-TO-END SPEECH SYNTHESIS WITH PRETRAINED MODELS

  • K. Inoue, S. Hara, M. Abe, T. Hayashi, R. Yamamoto, S. Watanabe
  • ICASSP2020, 2020/5

SPEECH PROCESSING Conference

Neural Text-to-Speech with a Modeling-by-Generation Excitation Vocoder

  • E. Song, M-J. Hwang, R. Yamamoto, J-S. Kim, O. Kwon, and J-M. Kim
  • INTERSPEECH2020, 2020/5

MACHINE LEARNING SECURITY & PRIVACY Conference

Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks

  • T. Takahashi
  • BigData2019, 2019/12

SPEECH PROCESSING Conference

Multichannel Loss Function for Supervised Speech Source Separation by Mask-based Beamforming

  • Y. Masuyama, M. Togami, T. Komatsu
  • INTERSPEECH2019, 2019/9

NLP Conference

FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance

  • W. Sakata, T. Shibata, R. Tanaka, and S. Kurohashi
  • SIGIR2019, 2019/7

SPEECH PROCESSING Conference

Spatial constraint on multi-channel deep clustering

  • M. Togami
  • ICASSP2019, 2019/5

SPEECH PROCESSING Conference

Multi-channel Itakura Saito Distance Minimization with deep neural network

  • M. Togami
  • ICASSP2019, 2019/5
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