Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix

Autor:	Fan, Jianyu, Nichols, Eric, Tompkins, Daniel, Mendez, Ana Elisa Mendez, Elizalde, Benjamin, Pasquier, Philippe
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Information Retrieval Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	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. Comment: Paper accepted for 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.09026 Zobrazit plný text záznamu View this record from Arxiv