基于深度学习的Siamese结构和成对存在矩阵的多标签声音事件检索
Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix
摘要：
真实的音景录音通常有多个声音事件同时发生，如汽车喇叭，引擎和人类的声音。声音事件检索是一种基于内容的搜索，旨在查找音频样本，类似于基于声学或语义内容的音频查询。现有技术的声音事件检索模型集中于仅发生一个声音事件的单标签音频记录，而不是多标签音频记录（即，在一个记录中发生多个声音事件）。为了解决后一个问题，我们提出了不同的深度学习架构，包括Siamese结构和Pairwise Presence Matrix。使用包含单标签和多标签音景录音的SONYC-UST数据集对网络进行训练和评估。实验结果表明了该模型的有效性。
英文摘要：
Realistic recordings of soundscapes often have multiple sound events co-occurring, such as car horns, engine and human voices. Sound event retrieval is a type of content-based search aiming at finding audio samples, similar to an audio query based on their acoustic or semantic content. State of the art sound event retrieval models have focused on single-label audio recordings, with only one sound event occurring, rather than on multi-label audio recordings (i.e., multiple sound events occur in one recording). To address this latter problem, we propose different Deep Learning architectures with a Siamese-structure and a Pairwise Presence Matrix. The networks are trained and evaluated using the SONYC-UST dataset containing both single- and multi-label soundscape recordings. The performance results show the effectiveness of our proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
