Unwanted variations in a signal are called noise. In both analog and digital systems, noise is any perturbation that disrupts the signal you actually want. It can come from inside the electronic components themselves, from the surrounding environment, or from the process of converting a signal from one form to another. The term applies broadly across electronics, telecommunications, audio engineering, and medical instrumentation.
What Noise Actually Means in Signal Terms
Noise is data without meaning. It rides on top of a useful signal and makes it harder to read, measure, or transmit accurately. A perfectly clean signal would contain only the information you intended. In reality, every signal picks up some amount of random or unwanted energy along the way.
The quality of a signal is measured using something called the signal-to-noise ratio (SNR), expressed in decibels (dB). A higher SNR means the desired signal is much stronger than the noise, giving you a cleaner result. A low SNR means noise is competing with or drowning out the information you care about. In audio contexts, for example, listeners prefer an SNR of about 1 to 2 dB for speech in background noise, meaning the speech only needs to be slightly louder than the noise to be comfortable.
Noise vs. Distortion vs. Interference
These three terms describe different problems. Noise is random and unpredictable. It has no pattern and no meaning. Distortion, by contrast, is a systematic change to the signal caused by the equipment or channel carrying it. A speaker that makes a guitar sound “crunchy” is distorting the signal in a repeatable way. Distortion includes things like magnitude changes and phase shifts introduced by imperfect transmission paths.
Interference sits somewhere in between. It refers to specific unwanted signals, often electromagnetic, that come from identifiable outside sources like radio transmitters, power lines, or other nearby electronics. Interference has structure and can sometimes be predicted and filtered out. Pure noise, being random, is harder to remove completely.
Where Internal Noise Comes From
Some noise is generated inside the electronic components themselves, and it can never be fully eliminated, only minimized.
- Thermal noise: The random motion of charged particles inside any resistive material. It increases with temperature, which is why sensitive instruments are sometimes cooled to extreme levels. This type is also called Johnson noise.
- Shot noise: Caused by the fact that electric current is made up of individual electrons, not a smooth continuous flow. Each electron crossing a junction arrives at slightly random times, creating tiny fluctuations.
- Flicker noise: Linked to imperfections in materials or manufacturing processes. It’s strongest at low frequencies and gradually decreases at higher ones, which is why engineers also call it 1/f noise.
- Burst noise: Sometimes called popcorn noise because of the way it sounds in audio circuits. It produces sudden, random jumps in voltage caused by defects in semiconductor materials, often from contamination during manufacturing.
These sources are always present to some degree in any electronic system. The goal isn’t to eliminate them entirely but to keep them small enough relative to the signal that they don’t cause problems.
External Sources of Noise
The environment contributes its own layer of unwanted signal variation. Lightning discharges produce broadband electromagnetic noise that affects radio frequencies. Any device that creates a spark or electrical arc, such as motors with brushes, relays, or switches, radiates electromagnetic energy that nearby circuits can pick up.
Power supplies and high-current switching equipment are common culprits in industrial and home settings. Even static electricity buildup, particularly in dry climates or air-conditioned buildings, can discharge into sensitive electronics and corrupt signals. Magnetic sensors face a unique problem called Barkhausen noise, caused by the jerky, random way magnetic domains realign during magnetization.
Noise in Digital Signals
Digital systems have their own distinct noise source: quantization noise. When an analog signal is converted to digital form, the smooth, continuous voltage must be mapped onto a limited set of discrete values. Each analog voltage gets rounded to the nearest digital step, and that rounding introduces a small error. This error is evenly distributed and acts like a layer of noise added to the original signal. Using more bits in the conversion (a higher resolution) makes the steps smaller and reduces quantization noise, but it never disappears entirely.
Noise in Medical Signals
Biological signals face particularly tricky noise problems because the human body produces multiple electrical signals simultaneously. When recording muscle activity (EMG), for instance, the most common source of contamination is the heart’s own electrical signal (ECG). Both signals share overlapping frequency ranges, which makes it difficult to separate them with simple filters. The closer the recording electrode is to the heart, the stronger this contamination becomes. Breathing and body movement add further noise layers that complicate diagnosis and interpretation.
How Noise Gets Reduced
On the hardware side, shielding cables and enclosures blocks external electromagnetic interference. Grounding provides a path for stray currents to drain away rather than contaminating the signal. Keeping signal wires away from power cables, using twisted-pair wiring, and selecting low-noise components all help at the design stage.
On the software side, filtering is the most common approach. A filter can be designed to pass only the frequency range where the desired signal lives while blocking frequencies dominated by noise. More advanced techniques decompose signals into components and selectively remove the noisy parts. Recent approaches use deep learning models that learn what “clean” signals look like and strip away noise without requiring detailed knowledge of the noise characteristics, essentially learning to denoise end-to-end from examples.
In practice, noise reduction uses a combination of these strategies. No single method eliminates all noise, but layering hardware precautions with software processing can push the signal-to-noise ratio high enough for reliable communication, accurate measurements, and clean audio.