This is possible in Aurora 2.0 because clean and noisy speech data are "parallel", i.e. each noisy utterance has a corresponding clean utterance.
One of the most efficient methods to process large data arrays is parallel processing based on specialized system solutions, in particular, on the neural-like parallel hierarchical systems.
Then, an N-points inverse discrete Fourier transform (IDFT) follows to produce the N dimensional data, which is parallel-to-serial converted.
Then, an N-points inverse discrete Fourier transform (IDFT) follows to produce the N dimensional data, which is parallel-to-serial (P/S) converted.
The data is somewhat parallel to the flow dialysis data that assume no interaction between EF-hands and, thus, the corresponding intrinsic binding constants were derived by statistics.
Since the double-reciprocal transformations of the original data set were parallel, the entire data set was globally fitted by non-linear regression to the equation describing a Ping Pong mechanism.
Although the lines generated by the data points were parallel, they were not identical.
The lines generated by data points were parallel but not identical, with a wide range of interception (-3.2 to -30.5) and a variable regression coefficient (1.0⌓3.1).
The serial data is converted into parallel streams by the serial-to-parallel (S/P) conversion block.
Parallel data buses allow much higher data rates than serial communication lines, because data is transmitted in parallel, that is, several bits are transmitted simultaneously.
