Audio and Signal Processing
Use Raspberry Pi Blockset along with DSP System Toolbox™, Signal Processing Toolbox™, and Audio Toolbox™ to implement advanced speech, audio, and signalling applications on Raspberry Pi hardware.
Topics
- Modularize Installation of Third-Party Packages and Libraries for Raspberry Pi Hardware
This section describes the workflow for downloading the core and optional application-based libraries and packages.
Apps
| Raspberry Pi Resource Monitor App | Monitor and manage Raspberry Pi resources |
Featured Examples
Get Started with Audio Signal Processing Using Raspberry Pi
Use Raspberry Pi® Blockset to get started with audio signal processing applications on the Raspberry Pi hardware. It also shows how to:
Audio Pitch Shift Using Audio Capture Playback
Shift the pitch of an audio signal on Raspberry Pi® hardware using audio capture and playback.
Audio Event Classification Using TensorFlow Lite on Raspberry Pi
Demonstrates audio event classification using a pretrained deep neural network, YAMNet, from TensorFlow™ Lite library on Raspberry Pi®. You load the TensorFlow Lite model and predict the class for the given audio frame on Raspberry Pi using a processor-in-the-loop (PIL) workflow. To generate code on Raspberry Pi, you use Embedded Coder®, Raspberry Pi Blockset and Deep Learning Toolbox Interface for TensorFlow Lite. Refer to Audio Classification and yamnet classification for more details on the YAMNet model description.
Simulate Audio Visualizer on Raspberry Pi Sense HAT
Simulate an audio visualizer on Sense HAT using Raspberry Pi® Blockset.
Spatialize Monaural Audio into 5.1 Channel Surround Sound Using Raspberry Pi
Spatialize a monaural audio signal into a 5.1 channel surround sound on Raspberry Pi® hardware by using a MATLAB Function block with Raspberry Pi® Blockset.
Shift Pitch of Audio Signal Using Raspberry Pi
Shift the pitch of an audio signal on Raspberry Pi® hardware by using a MATLAB Function block with Raspberry Pi Blockset.
Log Signals in MAT File Format Using Raspberry Pi
Log signals from a Simulink® model on Raspberry Pi® hardware in the MAT file format.
Detect Stop Signal Traffic Sign Using Raspberry Pi and Simulink
Use the Raspberry Pi® Blockset to detect a stop signal traffic sign. The example uses a cascade object detector to detect a stop sign using the Viola-Jones algorithm.
Speech Command Recognition on Raspberry Pi Using Simulink
Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition on Raspberry Pi®. In this example you develop a Simulink® model that captures audio from the microphone connected to the Raspberry Pi board and performs speech command recognition. You run the Simulink model on Raspberry Pi in External Mode and display the recognized speech command. For details about audio preprocessing and network training, see Train Deep Learning Network for Speech Command Recognition (Audio Toolbox).
