In the one-and-zero walled world of so-called “computer music” (undoubtedly a vague and even somewhat deprecating moniker), the turbulent chunks of synthetic hums, blips, and glitches are backed by a variety of paradigms. The most renowned artists in this field, such as Florian Hecker, who in his music “dramatizes space, time and self-perception in his sonic works by isolating specific auditory events in their singularity, thus stretching the boundaries of their materialization,” or Yasunao Tone, whose works are concerned with the sonic properties of transformed and converted media, might give the impression that this sort of music is a very academic or even notional pursuit. But ultimately the sound itself is what matters, and placing all of the credit on the theoretical back end is, in my opinion, fallacious and reductive. M, which collects two compositions by Lithuanian sound artist Gintas Kraptavičius—M (2012) and Mimicry (2017)—is a visceral opus that explores the staggering potentials of the artist’s palette of files, plugins, and effects. The album doesn’t concern itself with complex explanations—just the opposite, in fact; the only words on the packaging other than the credits and track titles is the famous Dalai Lama quote “Life is not easy.” Instead, like Network Glass, whose idiot/smiling I reviewed just the other day, Kraptavičius occupies the the much more universal dimension of isolated sound, focusing on the dizzying textural collages he crafts from the pulsing clouds of digital noise. Mimicry, which comprises the second part of the album, is very much a response to its precursor M; where the latter delves into dense, evolving clusters, the former takes on a volatility that feels much less composed, drawing power from its disarming unpredictability. “Mimicry4” presents the album’s closest flirtation with conventional beauty, rolling a loud, cathartic drone into the fray of frantic glitches, just one of the countless enrapturing sonic conversations with which Kraptavičius experiments. M is a decisive statement in raw data-driven music methodology, in itself an argument for a diversity of approaches.