Spectral Sound, Vol. 1 [Digipak] (CD - 2005)

Audio Mixer: Ryan Elliott.

Audio Remixers: Jörn Elling Wuttke; Roman Flügel; Bryan Zentz.

The first various-artists-on-CD release from one of the most adventurous and consistent dance labels of the early 2000s, this package comes with an unmixed disc of previously released tracks and exclusives, along with a blinding 33-track mix from label associate Ryan Elliott. The unmixed disc gets top billing, but it's eclipsed by the mix, even though the mix presents only a minute or two from selections that often deserve to be heard in full. Elliott takes it all in, whipping through major highlights from the label's catalog while including dozens of less-recognized productions. Points of interest: Lawrence's quick-step necro-house in "Five Leaves," Matthew Dear's depth-charge electronic body music in "Anger Management," and "Apple," where Hieroglyphic Being sets the sound of a rusted roller coaster swaying in a stiff wind to a cosmic thump. Above all, there's James T. Cotton's gut-punching "The Drain," a potential nausea-inducing experience in which the mechanized handclaps seem to be the only element not twisting and warping. Elliott knows these tracks inside out, covers lots of ground, and provides a trip of a trip through the label's back catalog. It's one of the best dance mixes made available in 2005. Not to be completely outstripped, disc one is attractive by itself. Osborne's "Bout Ready to Jak" combines humor and menace with a deep vocal command and even deeper bass pressure. Hieroglyphic Being's bounding warhead "Je Suis Musique" is cyborg tribal house, the opposite of the French disco-house implied with its title. Kept until the very end is the radio edit of Matthew Dear's delirious "Dog Days," the label's most popular track; two years on, it sounds more like a step forward than a look back. Like elders Kompakt and Playhouse, Spectral Sound has grown and thrived at the same time. Their releases are almost as reliable as the sleek, die-cut sleeves they come in. ~ Andy Kellman minimize