Time-frequency feature representation using energy concentration: An overview of recent advances

By Ervin Sejdic, Igor Djurovic, Jin Jiang. Digit. Signal Process., 2009

762 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/8fce466243540aa61b16088355d4555c9d2ed0b4

How researchers see this paper

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

In Signal Processing

1. John Peurifoy
2. Yann LeCun
3. Joaquín B. Ordieres Meré
4. Max Tegmark
5. Meiqing Wang
6. Xiaochen Zheng
7. Yoshua Bengio
8. Benigno Uria

Citations in the corpus

• Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “STFT is used to determine the sinusoidal frequency and phase content of local sections of a signal that changes over time [23,30].”
• Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs (2016, background): “The main task we consider is predicting the log-magnitude of future frames of a short-time Fourier transform (STFT) (Wisdom et al., 2016; Sejdi et al., 2009).”