Wavelet Transform With Tunable Q-Factor

By Ivan W. Selesnick. IEEE Transactions on Signal Processing, 2011

611 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/990b7c4d9b0dc21487e17600581e1efc58610975

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Miguel López-Benítez
2. Fei Ma
3. Enggee Lim
4. Tiantian Guo
5. Tongpo Zhang
6. Limin Yu

In Control and Systems Engineering

1. Miguel López-Benítez
2. Fei Ma
3. Enggee Lim
4. Tiantian Guo
5. Tongpo Zhang
6. Limin Yu

In Signal Processing

1. Miguel López-Benítez
2. Fei Ma
3. Enggee Lim
4. Tiantian Guo
5. Tongpo Zhang
6. Limin Yu

Citations in the corpus

• A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities (2022, methodology): “In 2009, Selesnick and Bayram [115], [118] constructed an over-complete fractional wavelet transform method, which is different from the early critical sampling mode.”
• An open access database for the evaluation of respiratory sound classification algorithms (2019, methodology): “Moreover, as a fast adaptive time-scale representation based feature extraction method, a tunable Q-factor wavelet transform (Selesnick 2011b) was also applied to low/high resonance and residual components, resulting in wavelet coefficients.”