Signal Processing

Signal processing is the practice of analyzing, manipulating, and transforming signals. Signals, in this context, refer to any time-varying data, which can represent various types of information, such as audio, video, images, sensor data, or any other data that varies with time or space.

There are two main types of signal processing:

1. Analog Signal Processing
   , in which signals are modeled as continuous waveforms, and
2. Digital Signal Processing
   , in which signals are represented as finite, discrete chunks of information, sample at regular intervals.

In data processing, specifically, signal processing generally refers to the practice of extracting and analyzing valuable information from digital signal data. Signal processing offers many different kinds of techniques and methods to do this, including filtering,

, smoothing, feature extraction, compression, and transformation. Read more in our dedicated signal processing article.

Signal processing can also refer to the process of analyzing any kind of time series data as if it were signal data. The techniques used in signal processing can then be used to clean, extract and analyze the time series data.

Signal Processing Resources

For more information and hands-on experience with signal processing, take a look at:

• Signal Processing in Real-time: the gap between ideal sampling and real-world data streams
• Applying a Gaussian filter on out-of-order signal data
• Upsampling your signal data stream