Voice Fingerprinting for Indoor Localization with a Single Microphone Array and Deep Learning
Voice Fingerprinting for Indoor Localization with a Single Microphone Array and Deep Learning
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DOI：
10.1145/3522783.3529528
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Shivenkumar Parmar;Xuyu Wang;Chao Yang;S. Mao
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作者：
Shivenkumar Parmar;Xuyu Wang;Chao Yang;S. Mao
随着物联网（IoT）的快速发展，用于语音辅助的智能扬声器在智能家居中变得越来越重要，它提供了一种新型的人机交互界面。使用麦克风阵列进行语音定位可以提高智能扬声器的性能，并实现许多新的物联网应用。为了解决复杂的室内环境，如非视距（NLOS）和多径传播的挑战，我们提出了语音指纹室内定位使用一个麦克风阵列。该系统由连接到Raspberry Pi和深度学习模型的ReSpeaker 6-mic圆形阵列套件组成，并在离线训练和在线测试阶段运行。在离线阶段，使用短时傅立叶变换（STFT）从音频数据获得的频谱图图像来训练模型。迁移学习用于加快训练过程。在在线阶段，top-K概率方法用于位置估计。实验结果表明，Inception-ResNet-v2模型在两种典型的家庭环境中均能获得满意的定位性能，且定位误差较小。
With the fast development of the Internet of Things (IoT), smart speakers for voice assistance have become increasingly important in smart homes, which offers a new type of human-machine interaction interface. Voice localization with microphone arrays can improve smart speaker's performance and enable many new IoT applications. To address the challenges of complex indoor environments, such as non-line-of-sight (NLOS) and multi-path propagation, we propose voice fingerprinting for indoor localization using a single microphone array. The proposed system consists of a ReSpeaker 6-mic circular array kit connected to a Raspberry Pi and a deep learning model, and operates in offline training and online test stages. In the offline stage, the models are trained with spectrogram images obtained from audio data using short-time Fourier transform (STFT). Transfer learning is used to speed up the training process. In the online stage, a top-K probabilistic method is used for location estimation. Our experimental results demonstrate that the Inception-ResNet-v2 model can achieve a satisfactory localization performance with small location errors in two typical home environments.