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Scalable and Efficient Neural Speech Coding: A Hybrid Design
We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine.
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding
Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals.
A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation.
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding
Speech codecs learn compact representations of speech signals to facilitate data transmission.
