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Found 6 papers, 1 papers with code
Generative De-Quantization for Neural Speech Codec via Latent Diffusion
In low-bitrate speech coding, end-to-end speech coding networks aim to learn compact yet expressive features and a powerful decoder in a single network.
Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding
Low and ultra-low-bitrate neural speech coding achieves unprecedented coding gain by generating speech signals from compact speech features.
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content
In the stereo-to-multichannel upmixing problem for music, one of the main tasks is to set the directionality of the instrument sources in the multichannel rendering results.
Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization
In this work, we learn to remix music directly by re-purposing Conv-TasNet, a well-known source separation model, into two neural remixing architectures.
Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)
Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others.
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity.
