Spain-Net: Spatially-Informed Stereophonic Music Source Separation

Petermann, Darius; Kim, Minje

doi:10.1109/icassp43922.2022.9746277

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2024

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“…A multi-scale T-F domain discriminator is employed to further improve audio quality in EnCodec [6]. In addition, traditional coding techniques such as psychoacoustics, sub-band coding, and band replication have also inspired the structural innovations and loss functions in end-to-end audio coding [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Neural Audio Coding with Deep Complex Networks

Ru,

Wang,

Jia

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes a transform domain audio coding method based on deep complex networks. In the proposed codec, the time-frequency spectrum of the audio signal is fed to the encoder which consists of complex convolutional blocks and a frequency-temporal modeling module to obtain the extracted features which are then quantized with a target bitrate by the vector quantizer. The structure of the decoder which reconstruct the time-frequency spectrum of the audio from quantized features is symmetrical to the encoder. In this paper, a structure combining the complex multi-head self-attention module and the complex long short-term memory is proposed to capture both frequency and temporal dependencies. Subjective and objective evaluation tests show the advantage of the proposed method.

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