Certain environmental noises have been associated with negative developmental
outcomes for infants and young children. Though classifying or tagging sound
events in a domestic environment is an active research area, previous studies
focused on data collected from a non-stationary microphone placed in the
environment or from the perspective of adults. Further, many of these works
ignore infants or young children in the environment or have data collected from
only a single family where noise from the fixed sound source can be moderate at
the infant's position or vice versa. Thus, despite the recent success of large
pre-trained models for noise event detection, the performance of these models
on infant-centric noise soundscapes in the home is yet to be explored. To
bridge this gap, we have collected and labeled noises in home soundscapes from
22 families in an unobtrusive manner, where the data are collected through an
infant-worn recording device. In this paper, we explore the performance of a
large pre-trained model (Audio Spectrogram Transformer [AST]) on our
noise-conditioned infant-centric environmental data as well as publicly
available home environmental datasets. Utilizing different training strategies
such as resampling, utilizing public datasets, mixing public and infant-centric
training sets, and data augmentation using noise and masking, we evaluate the
performance of a large pre-trained model on sparse and imbalanced
infant-centric data. Our results show that fine-tuning the large pre-trained
model by combining our collected dataset with public datasets increases the
F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84
(combined datasets) and Cohen's Kappa from 0.013 (public datasets) and 0.77
(collected datasets) to 0.83 (combined datasets) compared to only training with
public or collected datasets, respectively.

通过于婴儿身上佩戴的记录设备，我们收集并标记了 22 个家庭中家庭声音环境中的噪音，并探讨了一个大型预训练模型（Audio Spectrogram Transformer [AST]）在这些婴儿中心化的环境数据以及公开可用的家庭环境数据集上的性能表现。结果显示，将我们收集的数据集与公开数据集相结合进行精调，与仅使用公开或收集的数据集进行训练相比，F1 得分从 0.11（公开数据集）和 0.76（收集的数据集）提高到 0.84，Cohen's Kappa 从 0.013（公开数据集）和 0.77（收集的数据集）提升到 0.83。

婴儿中心家庭声音环境中的声音标记

Sound Tagging in Infant-centric Home Soundscapes

Autonomous driving systems are broadly used equipment in the industries and
in our daily lives, they assist in production, but are majorly used for
exploration in dangerous or unfamiliar locations. Thus, for a successful
exploration, navigation plays a significant role. Road detection is an
essential factor that assists autonomous robots achieved perfect navigation.
Various techniques using camera sensors have been proposed by numerous scholars
with inspiring results, but their techniques are still vulnerable to these
environmental noises: rain, snow, light intensity and shadow. In addressing
these problems, this paper proposed to enhance the road detection system with
filtering algorithm to overcome these limitations. Normalized Differences Index
(NDI) and morphological operation are the filtering algorithms used to address
the effect of shadow and guidance and re-guidance image filtering algorithms
are used to address the effect of rain and/or snow, while dark channel image
and specular-to-diffuse are the filters used to address light intensity
effects. The experimental performance of the road detection system with
filtering algorithms was tested qualitatively and quantitatively using the
following evaluation schemes: False Negative Rate (FNR) and False Positive Rate
(FPR). Comparison results of the road detection system with and without
filtering algorithm shows the filtering algorithm's capability to suppress the
effect of environmental noises because better road/non-road classification is
achieved by the road detection system. with filtering algorithm. This
achievement has further improved path planning/region classification for
autonomous driving system

本文提出使用滤波算法增强道路检测系统以解决环境噪声对自主驾驶系统路径规划 / 区域分类的影响，使用 False Negative Rate (FNR) 和 False Positive Rate (FPR) 进行检测并与未使用滤波算法进行比较。