Transformées
Signal Processing Toolbox™ propose des fonctions permettant de calculer des transformées directes et inverses largement utilisées, notamment la transformée de Fourier rapide (FFT), la transformée en cosinus discrète (DCT) et la transformée de Walsh-Hadamard. Extrayez les enveloppes des signaux et estimez les fréquences instantanées à l’aide du signal analytique. Analysez les signaux dans le domaine temps-fréquence. Étudiez les relations amplitude-phase, estimez les fréquences fondamentales et détectez la périodicité spectrale à l’aide du cepstre. Calculez des transformées de Fourier discrètes avec l’algorithme de Goertzel de second ordre.
Fonctions
Rubriques
Transformées de Fourier et en cosinus discrètes
- Discrete Fourier Transform
Explore the primary tool of digital signal processing. - Chirp Z-Transform
Use the CZT to evaluate the Z-transform outside of the unit circle and to compute transforms of prime length. - Discrete Cosine Transform
Compute discrete cosine transforms and learn about their energy compaction properties. - DCT for Speech Signal Compression
Use the discrete cosine transform to compress speech signals.
Transformées de Hilbert et de Walsh-Hadamard
- Hilbert Transform
The Hilbert transform helps form the analytic signal. - Analytic Signal for Cosine
Determine the analytic signal for a cosine and verify its properties. - Envelope Extraction
Extract the envelope of a signal using thehilbertandenvelopefunctions. - Analytic Signal and Hilbert Transform
Generate the analytic signal for a finite block of data using thehilbertfunction and an FIR Hilbert transformer. - Hilbert Transform and Instantaneous Frequency
Estimate the instantaneous frequency of a monocomponent signal using the Hilbert transform. Show that the procedure does not work for multicomponent signals. - Single-Sideband Amplitude Modulation
Perform single-sideband amplitude modulation of a signal using the Hilbert transform. Single-sideband AM signals have less bandwidth than normal AM signals. - Walsh-Hadamard Transform
Learn about the Walsh-Hadamard transform, a non-sinusoidal, orthogonal transformation technique. - Walsh-Hadamard Transform for Spectral Analysis and Compression of ECG Signals
Use an electrocardiogram signal to illustrate the Walsh-Hadamard transform.
Analyse cepstrale
- Complex Cepstrum — Fundamental Frequency Estimation
Use the complex cepstrum to estimate a speaker’s fundamental frequency. Compare the result with the estimate obtained with a zero-crossing method. - Cepstrum Analysis
Apply the complex cepstrum to detect echo in a signal.
Informations connexes
Sélection d՚exemples
DFT Estimation with the Goertzel Algorithm
Use the Goertzel method to implement a DFT-based Dual-Tone Multi-Frequency detection algorithm.
Discrete Walsh-Hadamard Transform
Apply the Walsh-Hadamard transform and its properties to spread-spectrum communications and ECG signal processing.
Single Sideband Modulation via the Hilbert Transform
Use the discrete Hilbert transform to implement single sideband modulation, an efficient form of AM.
