Transformationen
R2024bDSP System Toolbox™ bietet mehrere System objects und Blöcke zur Transformation von Streamingsignalen in der Zeitdomäne in die Frequenzdomäne und umgekehrt. Mit Techniken wie FFT (Fast Fourier Transform), Kurzzeit-Fourier-Transformation, Hochauflösungs-FFT, Wavelet-Transformation und den entsprechenden inversen Operationen können Sie das Signal in beiden Domänen analysieren. Anwendungen hierfür finden sich in zahlreichen Feldern. Ein Beispiel finden Sie unter Analyze a Subband of Input Frequencies Using Zoom FFT. Ein MathWorks® Schulungsmodul zu diesem Thema finden Sie unter Fourier-Analyse (MathWorks Teaching Resources).
Die DSP System Toolbox umfasst zudem den Wavelet Scattering-Block, der ein Framework für Wavelet-Time-Scattering in der Simulink® Umgebung bereitstellt. Verwenden Sie diesen Block, um Merkmale mit niedriger Varianz aus Echtwertdaten abzuleiten und verwenden Sie diese Merkmale für Machine-Learning- und Deep-Learning-Anwendungen. Weitere Informationen finden Sie unter Wavelet Scattering (Wavelet Toolbox). Der Wavelet Scattering-Block benötigt Wavelet Toolbox™.
Objekte
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 |
Blöcke
 | 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 (Seit R2022b) |
Themen
- 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.
Verwandte Informationen
Enthaltene Beispiele
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.
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.
