借我一只耳朵：利用配备麦克风阵列的机械臂进行语音增强
Lend me an Ear: Speech Enhancement Using a Robotic Arm with a Microphone Array
AI总结：
本文提出利用机械臂与麦克风阵列结合深度学习技术，通过动态调整麦克风位置提升语音增强性能，实验显示在不同信噪比条件下效果优于传统方法。
摘要：
在嘈杂环境中，语音增强性能显著下降，限制了语音控制技术在工业环境中的应用，如制造工厂。现有的语音增强解决方案主要依赖于先进的数字信号处理技术、深度学习方法或复杂的软件优化技术。本文介绍了一种新的增强策略，通过动态修改麦克风阵列的几何形状来适应变化的声学条件。一个十六个麦克风的阵列安装在具有七自由度的机械臂操作器上，麦克风分为四组，每组四个，包括一组靠近末端执行器的位置。系统通过调整机械臂关节角度来重新配置阵列，使末端执行器的麦克风更接近目标说话人，从而提高参考信号质量。该方法整合了声音源定位技术、计算机视觉、逆向动力学、最小方差无失真响应波束成形器和时频掩蔽，使用深度神经网络。实验结果表明，该方法优于其他传统录音配置，在多个输入信噪比条件下实现了更高的尺度不变信号失真比和更低的词错误率。
英文摘要：
Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial settings, such as manufacturing plants. Existing speech enhancement solutions primarily rely on advanced digital signal processing techniques, deep learning methods, or complex software optimization approaches. This paper introduces a novel speech enhancement robotic platform that can reconfigure the geometry of a microphone array and adapt to changing acoustic conditions. A sixteen-microphone array is mounted on a robotic arm manipulator with seven degrees of freedom. The microphones are divided into four groups of four, including one group positioned near the end-effector. The system reconfigures the array by adjusting the manipulator joint angles to place the end-effector microphones closer to the target speaker, thereby improving the reference signal quality. This proposed system is a multimodal sensing, reconfigurable audio capture device that integrates sound source localization techniques, computer vision, inverse kinematics, minimum variance distortionless response beamformer, and time-frequency masking using a deep neural network. Experimental results suggest that this approach outperforms other traditional recording configurations, achieving a higher average scale-invariant signal-to-distortion ratio and lower average word error rate across multiple input signal-to-noise ratio conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
