论文标题
基于距离的声音分离
Distance-Based Sound Separation
论文作者
论文摘要
我们提出了基于距离的声音分离的新任务，其中仅根据其与单个麦克风的距离进行分离。在辅助聆听设备的背景下，接近度为嘈杂环境中的声音选择提供了一个简单的标准，使用户可以专注于与本地对话相关的声音。我们通过训练神经网络来证明这种方法的可行性，从而相对于定义近距离和远处之间的边界的阈值距离，将近近声音与单个通道合成混音中的远处的声音分开。对于附近的单个扬声器和四个遥远的扬声器，该模型将尺度不变的信号与噪声比提高了4.4 dB，近距离声音为6.8 dB。
We propose the novel task of distance-based sound separation, where sounds are separated based only on their distance from a single microphone. In the context of assisted listening devices, proximity provides a simple criterion for sound selection in noisy environments that would allow the user to focus on sounds relevant to a local conversation. We demonstrate the feasibility of this approach by training a neural network to separate near sounds from far sounds in single channel synthetic reverberant mixtures, relative to a threshold distance defining the boundary between near and far. With a single nearby speaker and four distant speakers, the model improves scale-invariant signal to noise ratio by 4.4 dB for near sounds and 6.8 dB for far sounds.
