5 reasons you should use C++ for digital signal processing

Digital signal processing fundamentals#

Digital signal processing is used to measure, manipulate, and analyze digital input signals using DSP algorithms.

Some DSP use cases include:

• Sonar (sound navigation and ranging)
• Data compression
• Digital image processing
• Voice recognition
• Speech processing
• Audio signal processing

When you use DSP on audio signals, the original analog signal must first be converted to a digital signal. Once the digital signal is available, DSP can alter nearly every aspect of the audio to improve performance and quality.

The most common daily example of DSP is when you use your phone: The audio signal is compressed so voices are clear and understandable. Audio transmission during a phone call is analyzed and altered instantaneously through DSP.

Since DSP is an integral part of storing and manipulating audio, it’s no surprise we find a plethora of uses for it in a standard music studio. Digital audio workstations like ProTools HD use DSP. For example, DSP allows you to receive an analog guitar note and record it onto an interface where it can be stored, manipulated, edited, and transferred. Because DSP applications are programmable, you can customize and control the signals in a number of ways.

A DSP application must receive a great amount of data, identify it, then modify it with DSP algorithms to be effective. All this processing happens instantaneously, which requires high performance and efficient power usage.

5 reasons to use C++ for DSP#

1. Coding closer to hardware#

DSP is closely involved with hardware, so it’s helpful to use a language that can also code close to hardware. C++ gives us the ability to speak directly to hardware and optimize performance. This is great for DSP applications, where speed and efficiency are essential. With fewer guardrails than higher-level languages, C++ gives us the flexibility to reduce CPU overloading and latency.

In addition to customizing and manipulating DSP algorithms, we can fine-tune the way a computer’s hardware processes signals so as to optimize performance and CPU usage. This helps DSP applications run smoothly on any device, even if there is limited storage on the hardware or minimal power available.

2. Dynamic memory management#

Memory management refers to how a program uses computer memory. C++ uses dynamic memory management which means we must manually indicate where that memory is stored and also how it is removed. When memory management is automatic instead of dynamic, the process takes up more memory and CPU power.

Memory management in C++ allows for optimization for even larger scale applications. Since DSP produces a lot of data that needs to be sorted and stored, using C++ helps save valuable memory space and reduce the strain on processing speeds.

3. Support for fixed-point math#

It’s important to have support for fixed-point math in a DSP language, as we often convert floating points to fixed points when optimizing DSP. Fixed-point data features a decimal point in the same spot for each number, whereas floating-point numbers can have the decimal in various positions relative to the numerical value.

Fixed-point math increases ease of portability and decreases power required to run algorithms. The process of converting from floating point to fixed point can often lead to errors and be time-consuming. However, C++ has features that allow us to simplify the process of converting from floating points to fixed points. In doing so, we still end up with maintainable code, reduce errors, and can even automatically manipulate our decimal point positions.