2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-24.2
Paper Title Efficient Adversarial Audio Synthesis via Progressive Upsampling
Authors Youngwoo Cho, Korea Advanced Institute of Science and Technology (KAIST), South Korea; Minwook Chang, NCSOFT, South Korea; Sanghyeon Lee, Korea Advanced Institute of Science and Technology (KAIST), South Korea; Hyoungwoo Lee, Gerard Jounghyun Kim, Korea University, South Korea; Jaegul Choo, Korea Advanced Institute of Science and Technology (KAIST), South Korea
SessionMLSP-24: Applications in Audio and Speech Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper proposes a novel generative model called \toolname, which progressively synthesizes high-quality audio in raw-waveform. Progressive upsampling GAN (PUGAN) leverages the previous idea of the progressive generation of higher-resolution output by stacking multiple encoder-decoder architectures. Compared to the existing state-of-the-art model called WaveGAN, which uses a single decoder architecture, our model generates audio signals and converts them to a higher resolution in a progressive manner, while using a significantly smaller number of parameters, e.g., 3.17x smaller for 16 kHz output, than the WaveGAN. Our experiments show that the audio signals can be generated in real-time with comparable quality to that of WaveGAN with respect to the inception scores and human perception.