What technologies do I need to learn to build my own signal processing tools for sound engineers?

If you are interested in building your own signal processing tools for sound engineers, then you will need to be familiar with a number of different technologies. Some of the technologies that you will need to know include:

1. Csound - Csound is a software suite that allows sound engineers to create and evaluate sound models. It is widely used in the sound industry and can be used to create a wide variety of signal processing tools.

2. MATLAB - MATLAB is a powerful data analysis and visualization tool that is often used by sound engineers to perform mathematical operations on sound data. It can also be used for signal processing.

3. Signal Processing Toolbox for Windows - The Signal Processing Toolbox for Windows is a free software library that contains a variety of signal processing toolsets that can be used by sound engineers.

4. GNU Octave - GNU Octave is a free software platform that can be used for signal processing, mathematics, and data analysis. It is widely used in the scientific community and can be very useful when working with sound data.

If you want to turn your MATLAB prototype into a web app for signal processing, you’ll need a combination of technologies for different parts of the system.

For the backend, Python is an excellent choice because of libraries like NumPy, SciPy, and Matplotlib, which handle complex numbers, signal filtering, and plotting. Java also works, especially with libraries like JDSP or Apache Commons Math, though its DSP capabilities are more limited compared to Python. If performance is a concern, you could even use C++ or Rust compiled to WebAssembly for fast, browser-based computations.

To build the web app itself, you might consider frameworks like Spring Boot (Java) or FastAPI (Python) to handle the backend logic. These frameworks make it easy to expose your DSP functions as APIs.

On the frontend, you can use modern frameworks like React.js or Vue.js to create an interactive interface. Libraries like Plotly.js or D3.js can help you visualize signal data, making it easy for users to see results in real time.

In summary, a combination of Python for DSP, a web framework for APIs, and a JavaScript frontend for visualization would give you everything you need to build a powerful, web-based signal processing tool!