We usually call this bit resolution property of the quantization procedure “sample resolution” and it is measured in bits per sample.
For that reason, raw audio values usually vary from -2¹⁵ to 2¹⁵(1 bit used for the sign), however, as we will see later, this is usually normalized in the (-1, 1) range for the sake of simplicity. In the majority of the cases, 16 bits per sample are used to represent each quantized sample, which means that there are 2¹⁶ levels for the quantized signal. In other words, quantization is the process of reducing the infinite number precision of an audio sample to a finite precision as defined by a particular number of bits. Quantization is the process of replacing each real number, x(n), of the sequence of samples with an approximation from a finite set of discrete values. (This actually means that the discrete signal can capture a higher range of frequencies, namely from 0 to fs/2 Hz according to the Nyquist rule) 1Hz means one sample per second, so obviously higher sampling frequencies mean more samples per second and therefore better signal quality. Typical sampling frequencies are 8KHz, 16KHz and 44.1KHz. We use the sampling frequency (fs = 1/Ts) as the attribute that describes the sampling process. The interval between two successive discrete samples is the sampling period (Ts). Sampling is used to convert the time-varying continuous signal x(t) to a discrete sequence of real numbers x(n). This analog to digital conversion includes two processes: sampling and quantization. Similarly to images and videos, sound is an analog signal that has to be transformed to a digital signal, in order to be stored in computers and analyzed by software.
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