50 citations
- Naver (South Korea)KR4 papers
- Kyoto UniversityJP2 papers
- Nagoya UniversityJP2 papers
- Space Solutions (South Korea)KR2 papers
- Waseda UniversityJP2 papers
- Carnegie Mellon UniversityUS1 paper
- Google (United States)US1 paper
- Johns Hopkins UniversityUS1 paper
- Microsoft Research Asia (China)CN1 paper
- Microsoft Research (United Kingdom)GB1 paper
- Nagoya Institute of TechnologyJP1 paper
- Okayama UniversityJP1 paper
13 papers
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
Fumiyuki Kato, Tsubasa Takahashi, Shun Takagi +3
How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materia…
Acoustic Event Detection with Classifier Chains
Tatsuya Komatsu, Shinji Watanabe, Koichi Miyazaki +1
This paper proposes acoustic event detection (AED) with classifier chains, a new classifier based on the probabilistic chain rule. The proposed AED with classifier chains consists…
Unified Likelihood Ratio Estimation for High- to Zero-frequency N-grams
Masato Kikuchi, Kento Kawakami, Kazuho Watanabe +2
Likelihood ratios (LRs), which are commonly used for probabilistic data processing, are often estimated based on the frequency counts of individual elements obtained from samples.…
Refinement of Direction of Arrival Estimators by Majorization-Minimization Optimization on the Array Manifold
Robin Scheibler, Masahito Togami
We propose a generalized formulation of direction of arrival estimation that includes many existing methods such as steered response power, subspace, coherent and incoherent, as we…
Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss
Eunwoo Song, Ryuichi Yamamoto, Min-Jae Hwang +3
This paper proposes a spectral-domain perceptual weighting technique for Parallel WaveGAN-based text-to-speech (TTS) systems. The recently proposed Parallel WaveGAN vocoder success…
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…