Trasformate
R2026aDSP System Toolbox™ offre diversi System object e blocchi per trasformare i segnali in streaming dal dominio del tempo al dominio della frequenza e viceversa. Tecniche quali la trasformata di Fourier veloce (FFT), la trasformata di Fourier a breve termine, la FFT ad alta risoluzione, le trasformate wavelet e le corrispondenti operazioni inverse consentono di analizzare il segnale in entrambi i domini e trovano applicazione in diversi campi. Per un esempio, vedere Analyze a Subband of Input Frequencies Using Zoom FFT. Per un modulo didattico di MathWorks® su questo argomento, vedere Fourier Analysis (MathWorks Teaching Resources) (Analisi di Fourier (Risorse didattiche MathWorks)).
DSP System Toolbox offre anche il blocco Wavelet Scattering, che crea un framework per lo scattering wavelet temporale nell'ambiente di Simulink®. Utilizzare questo blocco per ricavare feature a bassa varianza da dati a valori reali e impiegare poi tali feature in applicazioni di Machine Learning e Deep Learning. Per ulteriori informazioni, vedere Wavelet Scattering (Wavelet Toolbox). Il blocco Wavelet Scattering richiede Wavelet Toolbox™.
Oggetti
dsp.FFT | Discrete Fourier transform |
dsp.IFFT | Inverse discrete Fourier transform (IDFT) |
dsp.ISTFT | Inverse short-time FFT |
dsp.STFT | Short-time FFT |
dsp.ZoomFFT
| High-resolution FFT of a portion of a spectrum |
Blocchi
 | Complex Cepstrum | Compute complex cepstrum of input |
 | FFT | Fast Fourier transform (FFT) of input |
 | IFFT | Inverse fast Fourier transform (IFFT) of input |
 | Inverse Short-Time FFT | Recover time-domain signals by performing inverse short-time fast Fourier transform (FFT) |
 | Magnitude FFT | Compute nonparametric estimate of spectrum using periodogram method |
 | Real Cepstrum | Compute real cepstrum of input |
 | Short-Time FFT | Nonparametric estimate of spectrum using short-time fast Fourier transform (STFT) method |
 | Zoom FFT | High-resolution FFT of a portion of a spectrum |
 | DCT | Discrete cosine transform (DCT) of input |
 | DWT | Compute discrete wavelet transform (DWT) of input or decompose signals into subbands with smaller bandwidths and slower sample rates |
 | IDCT | Inverse discrete cosine transform (IDCT) of input |
 | IDWT | Inverse discrete wavelet transform (IDWT) of input or reconstruct signals from subbands with smaller bandwidths and slower sample rates |
 | Wavelet Scattering | Model wavelet scattering network in Simulink (Da R2022b) |
Argomenti
- Transform Time-Domain Data into Frequency Domain
This example shows how to transform time-domain data into the frequency domain using the FFT block.
- Transform Frequency-Domain Data into Time Domain
When you want to transform frequency-domain data into the time domain, use the IFFT block.
- Linear and Bit-Reversed Output Order
Learn the meaning of linear and bit-reversed data order as used by the FFT and IFFT blocks.
- Analyze a Subband of Input Frequencies Using Zoom FFT
Compute the zoom FFT of a sine wave in Simulink.
- Speech Enhancement with Gain in Frequency Domain
Denoise a signal by applying selective gain on the signal in the frequency domain. Convert the denoised signal back into time domain and play the signal using Audio Device Writer.
- Variable-Size Signal Support DSP System Objects
List of System objects that support variable-sized signals in DSP System Toolbox.
Esempi in primo piano
Zoom FFT
Showcases zoom FFT, which is a signal processing technique used to analyze a portion of a spectrum at a high resolution. DSP System Toolbox™ offers this functionality in MATLAB® through the dsp.ZoomFFT System object™, and in Simulink® through the Zoom FFT block in the DSP System Toolbox library.
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
Deploy Wavelet Scattering and TensorFlow Lite Network on a Raspberry Pi Using Simulink
Generate and deploy Raspberry Pi® code to detect air compressor sounds using the Wavelet Scattering Simulink block and a pretrained deep learning network.
Detect Anomalies in ECG Data Using Wavelet Scattering and LSTM Autoencoder in Simulink
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
Risorse didattiche
Analisi di Fourier
Imparare i concetti di ampiezza, fase e frequenza e applicarli alle serie di Fourier e alle trasformate di Fourier continue e discrete.
