Background noises (BGN) come in many different shapes and sizes (figuratively speaking).
In layman's terms, BGN is often described as "office ventilation noise", "car noise", "street noise", "cocktail noise", "background music", etc.
Although this classification is practical for human understanding, the algorithms that model and produce comfort noise see things in more mathematical terms.
背景噪声种类繁多。
The most basic and intuitive property of BGN is its loudness.
This is referred to as the signal's energy level.
背景噪声的第一个属性: 声音大， 用来表征信号的能量等级。
Another less obvious property is the frequency distribution of the signal. For example, the hum of a running car and that of a vacuum cleaner can have the same energy level, yet they do not sound the same: these two signals have distinctly different spectrums.
第二个属性就是信号频域分布。
比如两个信号的能量等级相同，但是可能拥有不同的频谱。
The third important property of BGN is the variability over time of the first two properties.
When a BGN's energy level and spectrum are constant in time, it is said to be stationary. Some environments are prone to contain non-stationary BGN. The best example is street noise, in which cars come and go.
第三个重要属性就是能量等级和频谱分布是时变的。
Good CN algorithms must cope well with all types of BGN.
The regenerated comfort noise must match the original signal as closely as possible.
Furthermore, in instances when the CN model poorly matches the original signal, a good algorithm should try to minimize the degradation of subjective quality.
Today the trend in CN algorithms is to base them on a technique generically called spectral comfort noise (SCN), which tries to recreate the power and frequency spectrum of the original noise.
好的舒适噪声算法必须可处理各种类型的背景噪声。也就是说，生成的舒适噪声要尽可能接近原始背景噪声。
