Joao Felipe Santos

Representation Mixing for TTS Synthesis

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (see more)wever, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.

2019-05-11

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

doi.org

arxiv.org

Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask

Stylianos Ioannis Mimilakis

Konstantinos Drossos

Joao Felipe Santos

Gerald Schuller

Tuomas Virtanen

Yoshua Bengio

Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a le… (see more)arnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during training) a source-dependent mask and does not need the aforementioned post processing step. We introduce a recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter. Obtained results show an increase of 0.49 dB for the signal to distortion ratio and 0.30 dB for the signal to interference ratio, compared to previous state-of-the-art approaches for monaural singing voice separation.

2018-04-14

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

doi.org

arxiv.org

Deep Complex Networks