双耳音频重叠声事件的联合方向和接近分类

D. Krause, A. Politis, A. Mesaros
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引用次数: 2

摘要

声源接近和距离估计为声场景分析提供了重要的信息,在许多实际应用中引起了人们的极大兴趣。由于这两项任务具有互补的特性,因此确保两者之间的有效交互对于完整的听觉环境至关重要。在本文中,我们的目标是研究几种从双耳录音中执行联合接近和方向估计的方法,这两种方法都被定义为基于深度神经网络(dnn)的粗分类问题。考虑到双耳音频的局限性,我们提出了两种将球体分割成角度区域的方法,以获得一组方向类。对于每种方法,我们研究了不同的模型类型来获取到达方向(DoA)的信息。最后,我们提出了将接近性和方向估计问题结合成一个联合任务的各种方法,该任务提供了有关出现源的开始和偏移量的时间信息。实验进行了合成混响双耳数据集组成的多达两个重叠的声音事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Direction and Proximity Classification of Overlapping Sound Events from Binaural Audio
Sound source proximity and distance estimation are of great interest in many practical applications, since they provide significant information for acoustic scene analysis. As both tasks share complementary qualities, ensuring efficient interaction between these two is crucial for a complete picture of an aural environment. In this paper, we aim to investigate several ways of performing joint proximity and direction estimation from binaural recordings, both defined as coarse classification problems based on Deep Neural Networks (DNNs). Considering the limitations of binaural audio, we propose two methods of splitting the sphere into angular areas in order to obtain a set of directional classes. For each method we study different model types to acquire information about the direction-of-arrival (DoA). Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources. Experiments are performed for a synthetic reverberant binaural dataset consisting of up to two overlapping sound events.
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