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.
Live Q&A - Time-Frequency Analysis for Signal ProcessingLive Q&A with Wayne King for the talk titled Time-Frequency Analysis for Signal Processing
Data-Centric AI for Signal Processing Applications
Model-centric approaches to solve AI problems have been dominant in applications where large and high-quality datasets are available. Such approaches aim to improve model performance through the development of more complex architectures.
In signal processing applications, where data is usually scarce and noisy and where advanced models and architecture experts are hard to find, a potentially more fruitful approach is a data-centric one that focuses on improving the data to make simpler network architectures perform better. The idea is to enhance signal data by improving its labels, removing noise, and reducing variance and dimensionality. This idea can be extended to include transforming signals into domains where key features become more prominent and easier to distinguish.
In this talk I go over various examples that follow a data-centric approach to improve AI-model performance. I show how signal processing techniques like time-frequency transformations, filtering, denoising, multiresolution analysis, data synthesis, and data augmentation can be used to obtain state-of-the-art results with simpler AI architectures in the solution of signal classification, signal regression, and anomaly detection problems.
Convolution: A Practical Review
This session focuses on the practical aspects of convolution and FIR filtering and some of its applications, from algorithm exploration through to real-time signal processing.
We discuss the tradeoffs between time and frequency domain implementations, including latency and computational cost. For both domains we review the best-known implementation variants and some of the hardware technologies most used for performance acceleration.