waveletspectrogrammen

Wavelet Spectrogram Men are analytical tools used to visualize how the frequency content of a signal changes over time. They combine wavelet transforms with spectrogram techniques to provide a detailed time-frequency representation, especially useful for non-stationary signals such as audio, biomedical signals, and seismic data.

Wavelet transforms decompose a signal into components at various scales or resolutions, allowing for analysis of both high-frequency, short-duration events, and low-frequency, long-duration trends. When integrated into spectrograms, wavelets facilitate adaptive resolution, offering more precise insights into transient phenomena that traditional Fourier-based spectrograms may overlook.

A wavelet spectrogram is generated by applying a continuous wavelet transform across the signal, creating a two-dimensional map where the x-axis represents time, and the y-axis corresponds to frequency or scale. The intensity or color within the map indicates the amplitude of the signal at specific frequencies and times.

Compared to Short-Time Fourier Transform (STFT) spectrograms, wavelet spectrograms are more effective in analyzing signals with rapid changes, because wavelets can adjust their time-frequency resolution dynamically. This attribute makes them particularly advantageous in fields like speech processing, biomedical monitoring (e.g., EEG analysis), and geophysics.

Overall, wavelet spectrograms provide a powerful, flexible method for time-frequency analysis, offering detailed insights into complex, non-stationary signals while maintaining computational efficiency. Their ability to adapt resolution to signal characteristics makes them an essential tool in various scientific and engineering applications.