Human trafficking is a universal problem, persistent despite numerous efforts
to combat it globally. Individuals of any age, race, ethnicity, sex, gender
identity, sexual orientation, nationality, immigration status, cultural
background, religion, socioeconomic class, and education can be a victim of
human trafficking. With the advancements in technology and the introduction of
autonomous vehicles (AVs), human traffickers will adopt new ways to transport
victims, which could accelerate the growth of organized human trafficking
networks, which can make the detection of trafficking in persons more
challenging for law enforcement agencies. The objective of this study is to
develop an innovative audio analytics-based human trafficking detection
framework for autonomous vehicles. The primary contributions of this study are
to: (i) define four non-trivial, feasible, and realistic human trafficking
scenarios for AVs; (ii) create a new and comprehensive audio dataset related to
human trafficking with five classes i.e., crying, screaming, car door banging,
car noise, and conversation; and (iii) develop a deep 1-D Convolution Neural
Network (CNN) architecture for audio data classification related to human
trafficking. We have also conducted a case study using the new audio dataset
and evaluated the audio classification performance of the deep 1-D CNN. Our
analyses reveal that the deep 1-D CNN can distinguish sound coming from a human
trafficking victim from a non-human trafficking sound with an accuracy of 95%,
which proves the efficacy of our framework.

该研究的目标是针对自动驾驶汽车开发一种基于创新的音频分析的人口贩运侦测框架，使用一维卷积神经网络可以将人口贩卖受害者的声音从非人口贩卖声音中区分出来，准确率达 95%。

基于音频分析的自主驾驶交通工具人口贩卖检测框架

Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles

Mainstream Audio Analytics models are trained to learn under the paradigm of
one class label to many recordings focusing on one task. Learning under such
restricted supervision limits the flexibility of models because they require
labeled audio for training and can only predict the predefined categories.
Instead, we propose to learn audio concepts from natural language supervision.
We call our approach Contrastive Language-Audio Pretraining (CLAP), which
learns to connect language and audio by using two encoders and a contrastive
learning to bring audio and text descriptions into a joint multimodal space. We
trained CLAP with 128k audio and text pairs and evaluated it on 16 downstream
tasks across 8 domains, such as Sound Event Classification, Music tasks, and
Speech-related tasks. Although CLAP was trained with significantly less pairs
than similar computer vision models, it establishes SoTA for Zero-Shot
performance. Additionally, we evaluated CLAP in a supervised learning setup and
achieve SoTA in 5 tasks. Hence, CLAP's Zero-Shot capability removes the need of
training with class labels, enables flexible class prediction at inference
time, and generalizes to multiple downstream tasks.

提出一种基于自然语言监督学习的音频概念学习方法 CLAP，通过两个编码器和对比学习将音频和文本描述连接到多模态空间中，从而实现了零样本性能，其具有无需使用类别标签进行训练、预测灵活度高、具有多个下游任务通用性等优点。