788 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Kyoto UniversityJP5 papers
- Naver (South Korea)KR5 papers
- Johns Hopkins UniversityUS3 papers
- Nagoya UniversityJP3 papers
- Waseda UniversityJP3 papers
- Carnegie Mellon UniversityUS2 papers
- Hitachi (Japan)JP2 papers
- NTT (Japan)JP2 papers
- Space Solutions (South Korea)KR2 papers
- Tokyo Metropolitan UniversityJP2 papers
- Doshisha UniversityJP1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
10 papers · 1 filter
Surrogate Source Model Learning for Determined Source Separation
Robin Scheibler, Masahito Togami
We propose to learn surrogate functions of universal speech priors for determined blind speech separation. Deep speech priors are highly desirable due to their high modelling power…
TTS-by-TTS: TTS-driven Data Augmentation for Fast and High-Quality Speech Synthesis
Min-Jae Hwang, Ryuichi Yamamoto, Eunwoo Song +1
In this paper, we propose a text-to-speech (TTS)-driven data augmentation method for improving the quality of a non-autoregressive (AR) TTS system. Recently proposed non-AR models,…
Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminators
Ryuichi Yamamoto, Eunwoo Song, Min-Jae Hwang +1
This paper proposes voicing-aware conditional discriminators for Parallel WaveGAN-based waveform synthesis systems. In this framework, we adopt a projection-based conditioning meth…
Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization
Robin Scheibler
We propose a new algorithm for blind source separation (BSS) using independent vector analysis (IVA). This is an improvement over the popular auxiliary function based IVA (AuxIVA)…
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection
Sławomir Kapka
In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. Our method is an adaptation of the Class-Conditioned Auto-Encoder (C2AE) designed for th…
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi, Tsubasa Takahashi, Yang Cao +1
How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…
A study on more realistic room simulation for far-field keyword spotting
Eric Bezzam, Robin Scheibler, Cyril Cadoux +1
We investigate the impact of more realistic room simulation for training far-field keyword spotting systems without fine-tuning on in-domain data. To this end, we study the impact…
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks
Tsubasa Takahashi
Graph convolutional neural networks, which learn aggregations over neighbor nodes, have achieved great performance in node classification tasks. However, recent studies reported th…
Consistency-aware multi-channel speech enhancement using deep neural networks
Yoshiki Masuyama, Masahito Togami, Tatsuya Komatsu
This paper proposes a deep neural network (DNN)-based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal.…
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto +2
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully…