88 citations · 308 across the 88 of their papers we have counts for
12 papers · 1 filter
Mean-square-error-based secondary source placement in sound field synthesis with prior information on desired field
Keisuke Kimura, Shoichi Koyama, Natsuki Ueno +1
A method of optimizing secondary source placement in sound field synthesis is proposed. Such an optimization method will be useful when the allowable placement region and available…
Kernel Learning For Sound Field Estimation With L1 and L2 Regularizations
Ryosuke Horiuchi, Shoichi Koyama, Juliano G. C. Ribeiro +2
A method to estimate an acoustic field from discrete microphone measurements is proposed. A kernel-interpolation-based method using the kernel function formulated for sound field i…
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network
Takaaki Saeki, Shinnosuke Takamichi, Hiroshi Saruwatari
Incremental text-to-speech (TTS) synthesis generates utterances in small linguistic units for the sake of real-time and low-latency applications. We previously proposed an incremen…
Binaural rendering from microphone array signals of arbitrary geometry
Naoto Iijima, Shoichi Koyama, Hiroshi Saruwatari
A method of binaural rendering from microphone array signals of arbitrary geometry is proposed. To reproduce binaural signals from microphone array recordings at a remote location,…
Speech Enhancement by Noise Self-Supervised Rank-Constrained Spatial Covariance Matrix Estimation via Independent Deeply Learned Matrix Analysis
Sota Misawa, Norihiro Takamune, Tomohiko Nakamura +4
Rank-constrained spatial covariance matrix estimation (RCSCME) is a method for the situation that the directional target speech and the diffuse noise are mixed. In conventional RCS…
Multichannel Audio Source Separation with Independent Deeply Learned Matrix Analysis Using Product of Source Models
Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune +4
Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art multichannel audio source separation methods using the source power estimation based on deep neura…