深度学习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
提取训练深神经网络的特征和变换信号
Building deep learning models for real-world applications, including biomedical, audio, communications, and radar
通过硬件连接和模拟获取和生成信号数据集

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深度学习信号

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利用MATLAB的信号处理深入学习

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Signal Labeling and Dataset Management

使用MATLAB，您可以使用内置应用程序和特定于域的工具，可以帮助您使用标签和管理大量信号数据等任务准备您的信号数据，该方法过大以适合内存。

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Signal Labeling- Example
Label Audio Using Audio Labeler- Example
Import Data Using Datastore- Example
Waveform Segmentation Using Deep Learning- Example

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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Classify ECG Signals in Simulink Using Deep Learning- Example
雷达目标分类使用缩放和CNN / LSTM- Example
使用深度学习网络分离鸡尾酒会源分离- Example
Speech Command Recognition Using Deep Learning- Example

Preprocessing and Feature Extraction

信号预处理是用于提高整体信号质量的重要步骤。您可以使用内置功能和应用程序来清理信号并在培训深网络之前删除不需要的工件。您还可以从信号中提取标准和域特定功能以减少培训深度学习模型的数据维度。您还可以使用自动特征提取技术，例如小波散射，从信号和培训深网络中获得低方差功能。

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ECG信号分类使用长短期内存网络- Example
Automate Feature Extraction Using Wavelet Time Scattering- Example
使用长短期内存网络对性别进行分类- Example
Denoise Speech Using Deep Learning Networks- Example

Signal Generation and Acquisition

深度学习模型通常需要大量数据进行培训和验证。在某些情况下，数据的可用性可以是采用深度学习技术的限制因素。使用MATLAB和其他用于信号处理应用的附加组件，您可以模拟与实际情况密切匹配的合成数据，并使用深度学习技术开发模型。您可以将MATLAB与外部硬件接口以获取真实数据，以便通过早期原型验证培训的型号。

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Audio Connectivity and Audio Waveform Generation- Example
Modulation Classification with Deep Learning- Example

网络设计，培训和部署

Interactively design networks, speed up training using NVIDIA^®GPU，并更快地获得良好的结果。

设计

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^®代码，定位图带和CUDNN库