With the rise of AI audio processing (e.g., denoising, upmixing), the black-box nature of neural networks often results in "phasey" artifacts. Researchers are now explicitly training models to respect . They realize that while amplitude is easy to learn, the subtle temporal shifts created by all-pass networks are the difference between a "digital" and "natural" sounding AI.
In a broad sense, an "Allpassphase" could refer to a critical state in a system where every possible input or signal is processed and transmitted without any obstruction or alteration. This phase would theoretically allow for the unimpeded passage of all signals, frequencies, or energies through a system, medium, or interface. allpassphase
By applying slightly different allpassphase shifts to the left and right channels of a stereo mix (using a "phase shuffler"), you can alter the perceived width of an instrument. It doesn't sound like a slapback delay; it sounds like the instrument has moved forward or backward in the stereo field. This is a secret trick of mastering engineers for widening pad sounds or background vocals. With the rise of AI audio processing (e
For discrete-time (digital) domain: [ H(z) = \fraca + z^-11 + a z^-1, \quad |a| < 1 ] In a broad sense, an "Allpassphase" could refer