Time-Frequency Analysis for Signal Processing
Real-world signals often have frequency content that changes over time. Therefore, there is a need to describe signals jointly in time and frequency. Signal processing techniques for time-frequency analysis have been developed in response to this need and constitute a powerful tool for practitioners.
There is no unique or universally optimal time-frequency analysis technique. However, the proliferation of time-frequency analysis techniques should be regarded as an advantage. The signal processing engineer or data scientist is free to choose the method best suited to their type of data or application. In this talk we discuss several time-frequency analysis techniques and illustrate their application to common signal processing workflows. The theoretical underpinnings of these techniques and differences between them are highlighted to elucidate their strengths or weaknesses with respect to specific types of signals and applications. Finally, we discuss the important role that time-frequency analysis plays in AI applications with signals.