Time-Frequency Networks for Audio Super-Resolution

By Teck-Yian Lim, Raymond A. Yeh, Yijia Xu, Minh N. Do, Mark Hasegawa-Johnson. 2018

96 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Speech & Audio

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Citations in the corpus

SoundStream: An End-to-End Neural Audio Codec (2021, methodology): “Audio enhancement – Deep neural networks have been applied to different audio enhancement tasks, ranging from denoising [17]–[21] to dereverberation [22], [23], lossy coding denoising [2] and frequency bandwidth extension [1], [24].”
Real-Time Speech Frequency Bandwidth Extension (2020, methodology): “Inspired by the early success in image super-resolution [4], end-to-end audio-based solutions were proposed, based on wave-to-wave UNet [5], WaveNet [6, 7], hybrid time/frequency domain models [8].”
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI (2023, unknown): “Compared to previous work with 16kHz as the target frequency [5, 25, 39, 46, 53, 55, 56, 84], NU-Wave [49] is the first to synthesize 48kHz waveforms”
NU-GAN: High resolution neural upsampling with GAN (2020, methodology): “While [19] casts audio super-resolution as an audio translation task in the waveform (time) domain, [21] uses a time-frequency network (TFNet), processing the audio in both domains for better performance.”
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling (2021, unknown): “There have been several works that applied deep neural networks to audio super-resolution [1, 2, 3, 4, 5, 6, 7].”
Speech Super-Resolution Using Parallel WaveNet (2018, methodology): “The most popular objective used in these models is the (squared) Euclidean distance [1, 3, 4].”