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Found 4 papers, 2 papers with code
Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation
These architectures must be trained with tens of hours of annotated and high-quality speech data.
Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio
When individual audio stems are unavailable from production, Dialogue Separation (DS) can be applied to the final audio mixture to obtain estimates of these stems.
Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations
Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems.
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
