Abstract
In recent years, the relation between Sound Event Detection (SED) and Source Separation (SSep) has received a growing interest, in particular, with the aim to enhance the performance of SED by leveraging the synergies between both tasks. In this paper, we present a detailed description of JSS (Joint Source Separation and Sound Event Detection), our joint-training scheme for SSep and SED, and we measure its performance in the DCASE Challenge for SED in domestic environments. Our experiments demonstrate that JSS can improve SED performance, in terms of Polyphonic Sound Detection Score (PSDS), even without additional training data. Additionally, we conduct a thorough analysis of JSS’s effectiveness across different event classes and in scenarios with severe event overlap, where it is expected to yield further improvements. Furthermore, we introduce an objective measure to assess the diversity of event predictions across the estimated sources, shedding light on how different training strategies impact the separation of sound events. Finally, we provide graphical examples of the Source Separation and Sound Event Detection steps, aiming to facilitate the interpretation of the JSS methods.
Funder
Ministerio de Ciencia, Innovación y Universidades
European Regional Development Fund
Horizon 2020 Framework Programme
Publisher
Public Library of Science (PLoS)
Reference41 articles.
1. Sound Event Detection: A tutorial;A Mesaros;IEEE Signal Processing Magazine,2021
2. A Review of Deep Learning Based Methods for Acoustic Scene Classification;J Abeßer;Applied Sciences,2020
3. Drossos K, Adavanne S, Virtanen T. Automated audio captioning with recurrent neural networks. In: 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE; 2017. p. 374–378.
4. Detection and Classification of Acoustic Scenes and Events (DCASE Community);. http://dcase.community/.
5. Serizel R, Turpault N. Sound event detection from partially annotated data: Trends and challenges. In: IcETRAN conference. Srebrno Jezero, Serbia; 2019. Available from: https://hal.inria.fr/hal-02114652.