Perceptual phase quantization of speech

By Doh-Suk Kim. 2003

25 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Cognitive Neuroscience, Signal Processing

How researchers see this paper

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

In Civil and Structural Engineering

1. Hubert Siuzdak

In Cognitive Neuroscience

1. Benjamin J. Shannon
2. Hubert Siuzdak
3. Kuldip K. Paliwal
4. Deliang Wang
5. Mira Lilleholt Vik
6. Donald S. Williamson
7. Ashutosh Pandey

In Signal Processing

1. Nabarun Goswami
2. Naoya Takahashi
3. Yuki Mitsufuji
4. Purvi Agrawal
5. Benjamin J. Shannon
6. Hubert Siuzdak
7. Kamil K. Wójcicki
8. Kuldip K. Paliwal

Citations in the corpus

• Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis (2023, background): “Another line of work suggests perceptual phase quantization (Kim, 2003), which has proven promising in deep learning by treating the phase estimation problem as a classification problem (Takahashi et al., 2018).”
• The importance of phase in speech enhancement (2011, background): “…and Ney, 2001; Alsteris and Paliwal, 2005), speaker recognition (Nakagawa et al., 2007; Wang et al., 2009), speech coding (McAulay and Quatieri, 1995; Skoglund et al., 1997; Pobloth and Kleijn, 1999; Kim, 2003) and speech enhancement (Shi et al., 2006; Wó jcicki et al., 2008; Lu and Loizou, 2008).”
• PhaseNet: Discretized Phase Modeling with Deep Neural Networks for Audio Source Separation (2018, background): “The phase discretization or quantization has been intensively studied in speech/audio codings [27, 28].”