28-bits
the Bit depth
You will need many discrete bits. The number goes up because of the high number of amplitude levels.
The largest positive value that can be stored in 20 bits is calculated using the formula for the maximum value of an unsigned binary number, which is (2^n - 1), where (n) is the number of bits. For 20 bits, this is (2^{20} - 1 = 1,048,576 - 1 = 1,048,575). Therefore, the largest positive value that can be stored in 20 bits is 1,048,575.
The largest binary number that can be expressed with 16 bits is 1111111111111111, which is equivalent to 65,535 in decimal. This number uses all 16 bits set to 1. In general, for an n-bit binary number, the maximum value is (2^n - 1). Thus, for 16 bits, it is (2^{16} - 1 = 65,535).
The Word Size.
sample size"Bit depth. Please learn English before posting anything else on the internet." No... bit depth describes the number of bits of information recorded for each sample.Where as the number of bits used to store a sample sound is called the sample size. P.S. Before you attempt to insult somebody about learning English make sure your facts are right or you look like an idiot.sample sizeor maybesample depthor mabyecard sizeor maybesampling rate
If 8-bits were used to hold one number, the sample range would go from -128 to +127. Since sound samples can be both positive and negative you would use this range instead of the "bit math" 0 to 255. If 8-bits were used to hold one number, the sample range would go from -128 to +127. Since sound samples can be both positive and negative you would use this range instead of the "bit math" 0 to 255.
28-bits
Eight.
Rate refers to frequency, while size refers to the amount. Thus, Sampling Rate is measured in Hertz (number of times per second a sample is taken), and Sampling Size is measured in Bits (number of binary digits of information taken at a single time). Thus, if you Sample at 10 Hz/8 bits, that means you take 8 bits of information, 10 times per second.
To store the hexadecimal number FF, we need to convert it to binary first. FF in hexadecimal is equivalent to 1111 1111 in binary, which requires 8 bits to represent. Each hexadecimal digit corresponds to 4 bits in binary, so two hexadecimal digits (FF) require 8 bits to store.
the Bit depth
You will need many discrete bits. The number goes up because of the high number of amplitude levels.
log(number of generations) / log(2) Round the answer up.
8
Crumbly, bits of earth.