178 citations · 496 across the 34 of their papers we have counts for
10 papers · 1 filter
Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier
Li Li, Hirokazu Kameoka, Shoji Makino
This paper proposes an alternative algorithm for multichannel variational autoencoder (MVAE), a recently proposed multichannel source separation approach. While MVAE is notable in…
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko +1
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been…
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Hirokazu Kameoka, Kou Tanaka, Damian Kwasny +2
This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pit…
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…
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
Kou Tanaka, Takuhiro Kaneko, Nobukatsu Hojo +1
We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework…
Semi-blind source separation with multichannel variational autoencoder
Hirokazu Kameoka, Li Li, Shota Inoue +1
This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estim…
ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
This paper proposes a non-parallel many-to-many voice conversion (VC) method using a variant of the conditional variational autoencoder (VAE) called an auxiliary classifier VAE (AC…
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, wh…
Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms
Keisuke Oyamada, Hirokazu Kameoka, Takuhiro Kaneko +3
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude spectrograms do not conta…
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Xin Wang +4
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are ge…