TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation

Wang, Zhongqiu; Cornell, Samuele; Choi, Shukjae; Lee, Younglo; Kim, Byeong-Yeol; Watanabe, Soichi

doi:10.48550/arxiv.2209.03952

Cited by 1 publication

(1 citation statement)

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“…Regarding audio-to-audio generation, researchers have explored approaches like ConvTasNet [6] for speech enhancement, Make-An-Audio [8] model for audio in-painting, NeuralWarp [12] model for synthesizing binaural audio from a mono audio input, LASSNet [10] for sound extraction, and TF-GridNet [11] for speech separation.…”

Section: Audio-audio Generationmentioning

confidence: 99%

Research on artificial intelligence generated audio

Liu,

Chang,

Shen

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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AI-generated content(AIGC) has generated widespread media attention with its innovative tools such as ChatGPT and DALL-E, indicating the emergence of a new era of AI.[3] The intense media coverage has provided ample opportunities for the general public to gain insight into AIGC's operations. Audio plays an indispensable role as an information carrier in our daily work and life. With the continuous development of audio generation technology, the audio generation market has huge demand and potential, including music composition, speech synthesis, sound effect generation, and more. However, there remains a deficiency in a comprehensive analysis of artificial intelligence-generated audio (AIGA). As a result, a timely and systematic review of AIGA is necessary to keep up with the unstoppable trend of AI-powered audio creation. Most importantly, our work aims to fill this gap and provide a solid foundation for future research.

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