Alternatesample

Alternatesample is a term used in discussions of sampling and signal processing to describe a method that constructs an output sequence by alternately selecting samples from two sources or processing paths. The phrase is not standardized, so its exact meaning can vary by domain.

In its simplest form, if two input sequences A = {A0, A1, A2, ...} and B = {B0, B1, B2, ...} are available, the alternatesample operation yields an output O = {A0, B0, A1, B1, A2, B2, ...}. Variants include interleaving by time (O[2i] = A[i], O[2i+1] = B[i]), interleaving across channels (two streams representing different channels), or alternating between different processing paths or filters at successive samples.

Applications of alternatesample appear across fields. In audio and digital signal processing, it can create staggered or textured effects, or serve as a simple interleaver when combining two streams. In data augmentation for machine learning, alternatesampling may mix samples from two datasets to produce hybrid inputs. In parallel processing, it can realize a lightweight interleaving of data streams to improve throughput.

Implementation considerations include aligning input lengths, handling leftover samples when one source is shorter, and managing artifacts or discontinuities at the switch points. Depending on the context, alternating samples can introduce periodic artifacts or correlation that may be undesirable, so the technique is typically chosen for specific, well-understood effects rather than general-purpose sampling.

See also: interleaving, multiplexing, dithering, bootstrap sampling, data augmentation.