Speech coding based on a multi-layer neural network

By S. Morishima, H. Harashima, Y. Katayama. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

SoundStream: An End-to-End Neural Audio Codec (2021, methodology): “Autoencoder networks with quantization of hidden features were applied to speech coding early on [37].”
High Fidelity Neural Audio Compression (2022, positive): “Leveraging neural networks as trained transforms via an encoder-decoder mechanism has been explored by Morishima et al. (1990); Rippel et al. (2019); Zeghidour et al. (2021).”
End-To-End Optimized Speech Coding with Deep Neural Networks (2017, background): “in 1990 [7] – and the first to compete with a contemporary standard.”
LMCodec: A Low Bitrate Speech Codec with Causal Transformer Models (2023, methodology): “These networks leverage large amounts of training data while relaxing the assumptions made on the type of transformations applied by the system [3–10].”
Disentangling speech from surroundings in a neural audio codec (2022, background): “Recent progress in generative models of audio have allowed for the development of neural codecs [1, 2, 3, 4] that are competitive with traditional codecs.”