Digital signal processing (DSP) is a means by which we work with real-world signals, such as sounds, images, and temperatures, that have been digitized. One great language that you can use for DSP is C++.
C++ was developed as an extension of the C programming language. It's one of the oldest programming languages, but like its ancestor, C++ is still widely used today. And DSP is just one of its compelling use cases!
Today, we'll discuss 5 reasons to use C++ for digital signal processing.
We'll cover:
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 the 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.
4. Object-oriented programming
C++ supports object-oriented programming (OOP). The object-oriented programming paradigm allows us to model a system as a group of objects that we can manipulate. When we implement object-oriented programming, we can reduce redundancies, and therefore develop applications with less code.
OOP gives us much more maneuverability to customize and rearrange code. DSP applications often combine multiple filters to process a signal. Since each filter can be represented as an object, they can easily be removed or rearranged.
5. Compiled rather than interpreted
C++ is a compiled language, which helps improve the performance of an application. Compiled languages generally run faster than the alternative of interpreted languages. While some interpreted languages are also options for DSP, such as Python and MATLAB, they take more time to execute than compiled languages like C++. Since we know speed is an important requirement for DSP applications, the compiled nature of C++ serves in our favor.
Wrapping up and next steps
If you are just starting out as a developer and have an interest in DSP, C++ is a great language to learn. And with its widespread popularity, you can always transfer your C++ skills to help build other resource-intensive applications for other industries.
To help you master the C++ programming language, we've created the C++ for Programmers learning path. This path covers basic to advanced concepts in C++, including templates, object-oriented programming techniques, and the essentials of embedded programming.
If you're completely new to programming, you might want to consider Learn C++: The Complete Course for Beginners. This course starts with the absolute basics.
Continue learning about C++ on Educative
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