Abstract
Time-Frequency Analysis is an important tool that is widely used in engineering, applied sciences, and industrial applications. Several techniques have emerged in the last decade to increase the time-frequency resolution of such representations. However, in many cases, these techniques imply a heavy computational burden, which is particularly troublesome for embedded devices, edge computing, or online applications in general. In this paper, we introduce an adaptive algorithm based on the Synchrosqueezing Wavelet Transform, aimed at implementations on systems with constrained computational power. We test the performance of the algorithm on both synthetic and real-world signals. Additionally, we present a software architecture to implement the data processing algorithm keeping in mind the portability of the solution to different devices. A companion Python package has been made available for public use.
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