# [Develop your knowledge](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/)
- [CMSIS-DSP Python package](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-1/)
- [Set up environment](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-2/)
- [Load an audio file](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-3/)
- [Write a simple VAD](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-4/)
- [Write a noise suppression algorithm](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-5/)
- [Write the CMSIS-DSP Q15 implementation](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-6/)
- [Convert the CMSIS-DSP Python to C](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-7/)
- [Develop your knowledge](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-8/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_next-steps/)

## Project resources to expand your understanding
Now that you’ve seen how to build and port a complete DSP function using the CMSIS-DSP Python package, it’s a good idea to expand your understanding by looking at more examples. The CMSIS-DSP project provides many resources to help you deepen your knowledge.

## Study further examples
The [CMSIS-DSP python package](https://pypi.org/project/cmsisdsp/) and its [CMSIS-DSP python example folder](https://github.com/ARM-software/CMSIS-DSP/tree/main/PythonWrapper/examples) include tests, Jupyter notebooks, and documentation that highlight key differences between the Python and C APIs, helping you write more portable and efficient DSP code.

## Remaining issues
While the CMSIS-DSP Python package makes prototyping and conversion to C relatively easy, there are additional challenges when moving toward real-world applications. Here are a few remaining challenges to consider:

- This Learning Path has shown how the package helps to design and translate a DSP function working on a block of samples from Python to C. But in a real application, you receive a continuous stream of samples, not predefined blocks. You’ll need to split the stream into blocks before processing, and later recombine them to reconstruct the signal.
- Part of the difficulty in this Learning Path comes from splitting and recombining the signal. Porting the block-handling logic from Python to C introduces additional complexity.

[CMSIS-Stream](https://github.com/ARM-software/CMSIS-Stream) can help with this. It’s a platform-independent technology designed to simplify the use of block-processing functions with sample streams.

If you’re planning to deploy your DSP algorithms in streaming, real-time systems, it’s worth exploring CMSIS-Stream. It can greatly simplify handling streams of data with block-based processing, offering a clean and efficient way to bridge the gap between theory and deployment.

You should now have a better idea of what the CMSIS-DSP Python package is capable of, and how it relates to its C equivalent.