The channel, despite using companding, still has a given dynamic range so the input signal range needs to be appropriate if you want to avoid clipping or poor signal to noise ratio. Also, a number if sources could be passed through the same commander (say in a broadcast transmitter) so they need a reasonably constant loudness level.janu203 said:http://www.seas.ucla.edu/dsplab/sqc/over.html
It says we have to normalize a signal before it is fed to a compander which reduces its dynamic range. But why don't we directly feed the compander block without normalizing the function first?
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Normalisation of a signal or function is a common practice in many fields of science and engineering. It involves scaling the values of the signal to a common range, often between 0 and 1. This is done to eliminate any potential bias or differences in the magnitude of the data, allowing for easier comparison and analysis.
Normalising a signal can help to improve the accuracy and reliability of data analysis. By bringing all values to the same scale, it allows for easier comparison between different signals or functions. It can also help to reduce the effects of outliers and make the data more easily interpretable.
In some cases, normalising a signal may not be necessary. This largely depends on the specific application and the type of data being analysed. For example, if the data has already been standardised or normalised by the data collection method, further normalisation may not be needed. Additionally, if the differences in magnitude are not significant or do not affect the analysis, normalisation may not be necessary.
The process of signal normalisation involves dividing each data point by the maximum value of the signal, resulting in values between 0 and 1. Alternatively, it can also be achieved by subtracting the minimum value of the signal from each data point and then dividing by the range of the data. There are also other methods of normalisation, such as z-score normalisation, which involves subtracting the mean and dividing by the standard deviation.
While normalisation can be a useful tool in data analysis, it is important to note that it may not always be appropriate or effective. In some cases, normalisation can result in the loss of important information or distort the data. It is important for scientists to carefully consider the purpose and implications of normalising a signal before applying it to their data.