  We revisit the widely used bss eval metrics for source separation with an eye
out for performance. We propose a fast algorithm fixing shortcomings of
publicly available implementations. First, we show that the metrics are fully
specified by the squared cosine of just two angles between estimate and
reference subspaces. Second, large linear systems are involved. However, they
are structured, and we apply a fast iterative method based on conjugate
gradient descent. The complexity of this step is thus reduced by a factor
quadratic in the distortion filter size used in bss eval, usually 512. In
experiments, we assess speed and numerical accuracy. Not only is the loss of
accuracy due to the approximate solver acceptable for most applications, but
the speed-up is up to two orders of magnitude in some, not so extreme, cases.
We confirm that our implementation can train neural networks, and find that
longer distortion filters may be beneficial.
