1.9k citations · 2k across the 12 of their papers we have counts for
13 papers
Audio Difference Learning for Audio Captioning
Tatsuya Komatsu, Yusuke Fujita, Kazuya Takeda +1
This study introduces a novel training paradigm, audio difference learning, for improving audio captioning. The fundamental concept of the proposed learning method is to create a f…
Anomalous Sound Detection Using a Binary Classification Model and Class Centroids
Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda +1
An anomalous sound detection system to detect unknown anomalous sounds usually needs to be built using only normal sound data. Moreover, it is desirable to improve the system by ef…
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
Yafu Tian, Alexander Carballo, Ruifeng Li +1
Rich semantic information extraction plays a vital role on next-generation intelligent vehicles. Currently there is great amount of research focusing on fundamental applications su…
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara +1
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observati…
Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
Alexander Carballo, Abraham Monrroy, David Wong +6
In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and…
LIBRE: The Multiple 3D LiDAR Dataset
Alexander Carballo, Jacob Lambert, Abraham Monrroy-Cano +6
In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and l…
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection
Takenori Yoshimura, Tomoki Hayashi, Kazuya Takeda +1
This paper integrates a voice activity detection (VAD) function with end-to-end automatic speech recognition toward an online speech interface and transcribing very long audio reco…
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue +6
This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit named ESPnet-TTS, which is an extension of the open-source speech processing toolkit ESPnet. The toolkit sup…
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo +1
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full p…
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
Ekim Yurtsever, Yongkang Liu, Jacob Lambert +4
Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and…
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation
Shogo Seki, Hirokazu Kameoka, Li Li +2
This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multichannel Non-negative Matrix Factorization (MNMF) is one of powerful appr…
Back-Translation-Style Data Augmentation for End-to-End ASR
Tomoki Hayashi, Shinji Watanabe, Yu Zhang +4
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paire…
Multi-Head Decoder for End-to-End Speech Recognition
Tomoki Hayashi, Shinji Watanabe, Tomoki Toda +1
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model. In the multi-head attent…