8 accepted papers
This paper proposes a domain adaptation method for speech enhancement called Remixed2Remixed. The proposed method adopts Noise2Noise (N2N) learning to adapt models trained on artificially generated (out-of-domain: OOD) noisy-clean pairs of data to better separate real-world recorded (in-domain) nois…
This paper proposes a variational autoencoder (VAE)-based method for voice conversion (VC) on arbitrary source-target speaker pairs without parallel corpora, i.e., non-parallel any-to-any VC. One typical approach is to use speaker embeddings obtained from a speaker verification (SV) model as the con…
In speech synthesis, a generative adversarial network (GAN), training a generator (speech synthesizer) and a discriminator in a min-max game, is widely used to improve speech quality. An ensemble of discriminators is commonly used in recent neural vocoders (e.g., HiFi-GAN) and end-to-end text-to-spe…
Permutation invariant training (PIT) has recently attracted attention as a framework to achieve end-to-end time-domain audio source separation. Its goal is to train a separation network that takes a mixture signal as input and produces the J underlying source signals. Since the order of the output s…
This paper proposes a method called "Hungarian Block Permutation (HBP)" to solve the block permutation problem in frequency-domain multichannel audio source separation. Many methods for frequency-domain multichannel audio source separation are designed to simultaneously solve frequency-wise source s…
In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing. A mel-spectrogram vocoder must solve three inverse problems: recovery of the original-scale magnitu…
In this paper, we investigate two algorithms for variational autoencoder (VAE)-based underdetermined multichannel source separation. We previously extended the multichannel VAE (MVAE) method for determined multichannel source separation and proposed the generalized MVAE (GMVAE) method for underdeter…
In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichannel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribution of a conditional VAE (CVAE) as the generative model for the…