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.
翻译:在本文中,我们提出了一个开发高频无线电频道传输语音探测和增强算法的开放数据库,其中包括欧洲各地不同接收器记录的音频样本,所有接收器都对德国帕德伯恩基地站的同一单边带调频传输进行监测。传输和接收的语音信号在时间上是准确一致的,以便为深层学习检测和加强算法的监督培训提供平行数据。关于语音活动检测任务,提出了两个示范基线系统,一个基于统计方法,采用具有最低统计噪音下限估计值的多阶段维纳过滤器,另一个基于深层学习方法。