ディープラーニング

 

Deep Learning for Signal Processing

Deep learning offers new opportunities to develop predictive models to solve a wide variety of signal processing applications. MATLAB® supports the entire workflow—from exploration to implementation of signal processing systems built on deep networks. You can easily get started with specialized functionality for signal processing such as:  

  • Analyzing, preprocessing, and annotating signals interactively
  • Extracting features and transforming signals for training deep neural networks
  • Building deep learning models for real-world applications, including biomedical, audio, communications, and radar
  • Acquiring and generating signal datasets through hardware connectivity and simulations

“I don’t think MATLAB has any strong competitors for signal processing and wavelet analysis. When you add in its statistics and machine learning capabilities, it’s easy to see why nonprogrammers enjoy using MATLAB, particularly for projects that require combining all these methods.”

Ali Bahrami Rad, Aalto University

Signal Labeling and Dataset Management

With MATLAB, you can use built-in apps and domain-specific tools that can help you prepare your signal data with tasks such as labeling and managing large volumes of signal data that are too large to fit in memory.

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Time-Frequency Transforms

Time-frequency representations describe how the spectral content in a signal evolves as a function of time. You can train deep learning networks which can identify and extract patterns from the time-frequency representations. You can also choose from a variety of techniques that can generate time-frequency representations for signals, including spectrogram, mel-frequency spectrogram, Wigner-Ville, and continuous wavelet transform (or scalograms).

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Preprocessing and Feature Extraction

Signal preprocessing is a crucial step for enhancing overall signal quality. You can use built-in functions and apps for cleaning up signals and remove unwanted artifacts before training a deep network. You can also extract standard and domain-specific features from signals to reduce data dimensionality for training deep learning models. You can also use automatic feature extraction techniques, such as Wavelet Scattering, to obtain low-variance features from signals and train deep networks.

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Signal Generation and Acquisition

Deep learning models typically require large amounts of data for training and validation. In certain situations, the availability of data can be the limiting factor in adopting deep learning techniques. With MATLAB and other add-ons for signal processing applications, you can simulate synthetic data that closely matches real-world scenarios and develop models using deep learning techniques. You can interface MATLAB with external hardware to acquire real-world data for the purposes of validating your trained models via early prototypes.

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Network Design, Training, and Deployment

Interactively design networks, speed up training using NVIDIA® GPUs, and get to good results faster.

Design

Import pretrained models using ONNX™, then use the Deep Network Designer app to add, remove, or rearrange layers.

Training

Whether you are using one GPU, multiple GPUs, GPUs on cloud, or NVIDIA DGX, MATLAB supports multi-GPU training with one line of code.

Deployment

Deploy deep learning models anywhere. Automatically generate code to run natively on ARM® and Intel® MKL-DNN. Import your deep learning models and generate CUDA® code, targeting TensorRT and CuDNN libraries

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