Who is this for?
This is an advanced topic for developers looking to integrate the CMSIS-DSP library into their applications using Python.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Use the CMSIS-DSP Python package to prototype DSP algorithms.
- Understand how the Python API maps to the C implementation.
- Build and port a complex DSP application using CMSIS-DSP.
Prerequisites
Before starting, you will need the following:
- Familiarity with Python and digital signal processing concepts.
- Working knowledge of C.
- Prior exposure to CMSIS-DSP.
- Python installed on your machine.
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
You’ll prototype a DSP pipeline in Python with the
cmsisdsp package, then relate those steps to CMSIS-DSP C for embedded targets. First, you’ll set up a Python environment and Jupyter notebook, load a sample “yes/no” audio clip with background noise, and implement a simple energy-based voice activity detector with a tunable threshold. Then, you’ll develop a noise-suppression routine using overlapping frames and a Hanning window generated by the CMSIS-DSP Python API, alongside a NumPy reference. You’ll validate the approach by listening to processed audio and inspecting plots, then map Python calls to equivalent C functions for Cortex-M and Cortex-A implementations.Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
In your activated environment, start Python and import
cmsisdsp as dsp and numpy. Call dsp.arm_hanning_f32 on a small window length and confirm it returns an array without errors.Confirm the notebook kernel is running and that the Audio object is the last expression in the cell so Jupyter displays it. Rerun the cells that define filedata and the Audio widget, then check the cell output.
Start with a moderate value, then iterate by listening to the result. Aim to remove noise between words without clipping spoken segments. Adjust the threshold and rerun until the segmentation matches what you expect.
Plot the Hanning window as shown and check that it tapers near zero at both ends. Inspect frame indices to confirm overlap and listen for smooth transitions without clicks at frame boundaries.
Playback should have reduced background noise during pauses while keeping the “yes” and “no” utterances clear. If speech sounds distorted or noise persists, revisit the VAD threshold and window length.