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Ryuki Tachibana

7 accepted papers

2021

Data-Efficient Framework for Real-World Multiple Sound Source 2d Localization

Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar +1
ICASSP 2021accepted

Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acoustic conditions and micro-phone array layouts. One can leverage acoustic simulators to inexpensively generate labeled tra…

Cited by 0Source ↗Scholar ↗
2018

MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

Asim Munawar, Giovanni De Magistris, Tu-Hoa Pham, Daiki Kimura, Michiaki Tatsubori, Takao Moriyama +2
ICRA 2018poster

This paper describes a framework called MaestROBe It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms…

Cited by 25Source ↗Scholar ↗
2018

OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World

Tu-Hoa Pham, Giovanni De Magistris, Ryuki Tachibana
ICRA 2018poster

While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesi…

Cited by 196Source ↗Scholar ↗
2017

Deep reinforcement learning for high precision assembly tasks

Tadanobu Inoue, Giovanni De Magistris, Asim Munawar, Tsuyoshi Yokoya, Ryuki Tachibana
IROS 2017poster

The high precision assembly of mechanical parts requires precision that exceeds that of robots. Conventional part-mating methods used in the current manufacturing require numerous parameters to be tediously tuned before deployment. We show how a robot can successfully perform a peg-in-hole task with…

Cited by 379Source ↗Scholar ↗
2017

Effective joint training of denoising feature space transforms and Neural Network based acoustic models

Takashi Fukuda, Osamu Ichikawa, Gakuto Kurata, Ryuki Tachibana, Samuel Thomas, Bhuvana Ramabhadran
ICASSP 2017accepted

Neural Network (NN) based acoustic frontends, such as denoising autoencoders, are actively being investigated to improve the robustness of NN based acoustic models to various noise conditions. In recent work the joint training of such frontends with backend NNs has been shown to significantly improv…

Cited by 0Source ↗Scholar ↗
2016

Convolutional neural network pre-trained with projection matrices on linear discriminant analysis

Takashi Fukuda, Osamu Ichikawa, Ryuki Tachibana
ICASSP 2016accepted

Recently, the hybrid architecture of a neural network (NN) and a hidden Markov model (HMM) has shown significant improvement on automatic speech recognition (ASR) over the conventional Gaussian mixture model (GMM)-based system. The convolutional neural network (CNN), a successful NN-based system, ca…

Cited by 4Source ↗Scholar ↗
2016

Speech recognition robust against speech overlapping in monaural recordings of telephone conversations

Masayuki Suzuki, Gakuto Kurata, Tohru Nagano, Ryuki Tachibana
ICASSP 2016accepted

Monaural (single-channel) recording is sometimes used for telephone conversations in call centers. Generally speaking, the accuracy of automatic speech recognition of a monaural recording is worse than that of the multi-channel recording of the same conversation where each speaker's voice is separat…

Cited by 0Source ↗Scholar ↗