  In this paper we present an open database for the development of detection
and enhancement algorithms of speech transmitted over HF radio channels. It
consists of audio samples recorded by various receivers at different locations
across Europe, all monitoring the same single-sideband modulated transmission
from a base station in Paderborn, Germany. Transmitted and received speech
signals are precisely time aligned to offer parallel data for supervised
training of deep learning based detection and enhancement algorithms. For the
task of speech activity detection two exemplary baseline systems are presented,
one based on statistical methods employing a multi-stage Wiener filter with
minimum statistics noise floor estimation, and the other relying on a deep
learning approach.
