Human and/or asset tracking using an attached sensor units helps understand their activities. Most common indoor localization methods for human tracking technologies require expensive infrastructures, deployment and maintenance. To overcome this problem, environmental sounds have been used for infrastructure-free localization. While they achieve room-level classification, they suffer from two problems: low signal-to-noise-ratio (SNR) condition and non-uniqueness of sound over the coverage area. A microphone localization method was proposed using supervised spectral decomposition and spatial likelihood to solve these problems. The proposed method was evaluated with actual recordings in an experimental room with a size of 12 x 30 m. The results showed that the proposed method with supervised NMF was robust under low-SNR condition compared to a simple feature (mel frequency cepstrum coefficient: MFCC). Additionally, the proposed method could be easily integrated with prior distribution, which is available from other Bayesian localizations. The proposed method can be used to evaluate the spatial likelihood from environmental sounds.

中文翻译：

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基于谱分解和空间似然模型的室内环境声音无基础设施定位

使用附加传感器单元跟踪人员和/或资产有助于了解他们的活动。用于人体跟踪技术的最常见的室内定位方法需要昂贵的基础设施、部署和维护。为了克服这个问题，环境声音已被用于无基础设施的定位。虽然它们实现了房间级分类，但它们面临两个问题：低信噪比 (SNR) 条件和覆盖区域内声音的非唯一性。提出了一种使用监督谱分解和空间似然的麦克风定位方法来解决这些问题。所提出的方法在尺寸为 12 x 30 m 的实验室中通过实际录音进行了评估。结果表明，与简单特征（梅尔频率倒谱系数：MFCC）相比，所提出的有监督 NMF 方法在低 SNR 条件下具有鲁棒性。此外，所提出的方法可以轻松地与先验分布集成，这可以从其他贝叶斯本地化中获得。所提出的方法可用于评估环境声音的空间可能性。