Noise is unwanted, unpleasant, loud, or disruptive audio that is not part of the creative intent when audio is captured. These sounds frequently have no structure and are disruptive in perceiving media.
It is common to categorize noise as:
- stationary or static noise
- non-stationary noise
Stationary noise is often characterized by constant, steady, low volume background noise. This type of noise can come from heating systems, air conditioners, fans, nearby computer equipment, or even the recording equipment itself such as microphone hiss or electric hum from a power-line.
Detecting this type of noise can be done through an analysis of the sound characteristics such as variations within frequency bands over time. There are different techniques for this type of digital signal processing to remove or subtract this type of noise.
Non-stationary noise is often irregular, transient, and appears infrequently or at a cyclical time in media. Some examples of this type of noise can be more varied:
- a dog barking
- birds chirping
- keyboard clicks
- an ambulance driving by
- a book falling off a desk
- speech such as babble, hubbub, or a cacophony of voices
- throbbing or vibration sounds that are cyclical
All of these non-stationary sounds are considered unwanted noise too, but are not easy to distinguish from the sound profile itself. Another way to think of it is the inverse of how to look for stationary sounds. Instead of detecting noise, we use machine learning algorithms that know how to elevate the sounds desired through speech isolation of spoken words in certain types of media.
Updated over 1 year ago