In the social Web era, crowdfunding has become an increasingly important channel for entrepreneurs to raise funds from the crowd for their start-up projects. Previous studies have examined various factors, such as textual information of projects and social capital of investors. However, multimedia information on projects such as audio information was rarely studied for analysing crowdfunding successes. This paper designs a novel audio analytics-based deep learning framework that can extract audio features to predict the fundraising outcomes of these projects. In the proposed framework, we suggest transfer learning to train our models, and multi-task learning to extract the deep features of audios. With the proposed features, our model achieves an 8.28% improvement in F1 and a 7.35% AUC comparing to baselines.

在社交网络时代，众筹已成为企业家为启动项目从人群中筹集资金的越来越重要的渠道。先前的研究已经研究了各种因素，例如项目的文字信息和投资者的社会资本。然而，很少研究诸如音频信息之类的项目上的多媒体信息来分析众筹成功。本文设计了一种基于音频分析的新型深度学习框架，该框架可以提取音频功能以预测这些项目的筹款结果。在提出的框架中，我们建议通过转移学习来训练我们的模型，并建议通过多任务学习来提取音频的深层特征。与建议的功能相比，我们的模型与基线相比，F1的改进为8.28％，AUC为7.35％。