Version 8.2, part of Release 2019a, includes the following enhancements:

• Signal Labeling: Label signals interactively and visualize labeled signals
• Time-Frequency Analysis: Compute short-time Fourier transforms and inverse short-time Fourier transforms
• Signal Analyzer App: Remove trends from signals and estimate their envelopes
• Signal Analyzer App: Enhanced management of multichannel signals
• C/C++ Code Generation Support: Generate code for filter design, spectral analysis, and spectral windowing (requires MATLAB Coder)

See the Release Notes for details.

Version 8.1, part of Release 2018b, includes the following enhancements:

• Signal Analyzer App: Preprocess signals using user-defined functions
• Signal Analyzer App: Change sample rates of signals and convert nonuniformly sampled signals to uniformly sampled signals
• Time-Frequency Analysis: Analyze signals using the Wigner-Ville distribution
• Deep Learning Example: Identify morphological features of signals using recurrent neural networks

See the Release Notes for details.

Version 8.0, part of Release 2018a, includes the following enhancements:

• Signal Analyzer App: Preprocess signals by smoothing and filtering
• Signal Analyzer App: Detect transients and perform time-frequency analysis using scalogram view
• One-Step Filtering: Filter signals using lowpass, highpass, bandpass, and bandstop responses
• Time-Frequency Analysis: Perform empirical mode decomposition, Hilbert-Huang transform, and instantaneous frequency estimation
• Time-Frequency Analysis: Estimate kurtogram, spectral kurtosis, and spectral entropy
• poctave Function: Compute 1/N octave spectra and perform octave smoothing
• Rotating Machinery: Estimate and track rotational speed from vibration signals
• Deep Learning Example: Classify signals using long short-term memory networks

See the Release Notes for details.

Version 7.5, part of Release 2017b, includes the following enhancements:

• Signal Analyzer App: Analyze sporadic signals with persistence spectrum and sharpen time-frequency estimates using reassignment
• Signal Analyzer App: Extract and export signal regions of interest
• Signal Analyzer App: Generate MATLAB scripts to automate analysis
• pspectrum Function: Analyze power spectrum, spectrogram, and persistence spectrum of signals
• Rotating Machinery: Remove noise coherently with time-synchronous averaging and analyze wear using envelope spectra
• Modal Analysis: Use parametric methods for FRF and modal parameter estimation
• Fatigue Analysis: Perform high-cycle rainflow counting
• findchangepts Function: Find changepoints in spectrograms and other multivariate signals

See the Release Notes for details.

Version 7.4, part of Release 2017a, includes the following enhancements:

• Signal Analyzer App: Perform time-frequency analysis using spectrogram view
• Signal Analyzer App: Analyze timestamped signals
• Modal Analysis: Estimate frequency-response functions and modal parameters of mechanical systems
• Cross-Spectrogram: Compare time-frequency content of nonstationary signals
• MIMO Spectral Analysis: Estimate cross-spectral density and coherence for multi-input/multi-output systems
• Transfer Function Estimation: Compute unbiased estimates for SISO and MIMO systems containing additive input noise
• dct and idct Functions: Compute four standard types of discrete cosine transform
• Code Generation Support: Generate code for an expanded set of Signal Processing Toolbox functions

See the Release Notes for details.

Version 7.3, part of Release 2016b, includes the following enhancements:

• Signal Analyzer App: Perform time- and frequency-domain analysis of multiple time series
• Similarity Matching: Find patterns in data using edit distance or dynamic time warping
• Order Analysis: Track orders and extract waveforms to analyze rotational machinery
• Fourier Synchrosqueezing Transform: Obtain sharp time-frequency estimates and extract signal modes

See the Release Notes for details.

Version 7.2, part of Release 2016a, includes the following enhancements:

• Gap Filling: Reconstruct missing samples using autoregressive modeling
• Changepoint Detection: Find abrupt changes and statistical shifts in signals
• Dynamic Time Warping: Stretch, align, and compare signals with different time scales
• Reassigned Periodogram: Sharpen the frequency localization of spectral estimates

See the Release Notes for details.