A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

Ronchini, Francesca; Serizel, Romain

doi:10.1109/icassp43922.2022.9747577

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…For DCASE task 4, two scenarios are considered, as described in Ronchini et. al [20]. The PSDS is indicated throughout the paper as PSDS_1 when evaluated on scenario 1 and PSDS_2 when evaluated on scenario 2, regardless of the system.…”

Section: Analysis Setup and Evaluation Metricsmentioning

confidence: 99%

“…Starting from 2018, the objective of DCASE task 4 has been to investigate SED using a heterogeneous dataset featuring audio soundscapes with varying levels of label detail [19]. Throughout the years, alongside the ranking procedure, the evaluation of diverse submissions in practical scenarios has been crucial to gaining insights into the systems' performance [20][21][22][23]. In DCASE task 4, as in many other audio tasks, there has been a consistent trend of models steadily increasing in parameter complexity, frequently incorporating ensemble techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In 2022, we asked participants to report the energy consumption of their systems both at training and test time [20]. We also introduced a new energy consumption metric [24], based on CodeCarbon toolkit [17].…”

Section: Introductionmentioning

confidence: 99%

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A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

Ronchini¹,

Serizel²

2022

Preprint

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This paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task. The submissions are evaluated according to the two polyphonic sound detection score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to make an analysis on whether submissions are designed to perform fine-grained temporal segmentation, coarse-grained temporal segmentation, or have been designed to be polyvalent on the scenarios proposed.We study the solutions proposed by participants to analyze their robustness to varying level target to non-target signal-to-noise ratio and to temporal localization of target sound events. A last experiment is proposed in order to study the impact of non-target events on systems outputs. Results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and, with the help of specific data augmentation methods, they are more robust to time localization of the original event. Results of the last experiment display that systems tend to spuriously predict short events when non-target events are present. This is particularly true for systems that are tailored to have a fine segmentation.

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Section: Analysis Setup and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

Ronchini¹,

Serizel²

2022

Preprint

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Sound Event Detection for Human Safety and Security in Noisy Environments

et al. 2022

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The objective of a sound event detector is to recognize anomalies in an audio clip and return their onset and offset. However, detecting sound events in noisy environments is a challenging task. This is due to the fact that in a real audio signal several sound sources co-exist and that the characteristics of polyphonic audio are different from isolated recordings. It is also necessary to consider the presence of noise (e.g. thermal and environmental). In this contribution, we present a sound anomaly detection system based on a fully convolutional network which exploits image spatial filtering and an Atrous Spatial Pyramid Pooling module. To cope with the lack of datasets specifically designed for sound event detection, a dataset for the specific application of noisy bus environments has been designed. The dataset has been obtained by mixing background audio files, recorded in a real environment, with anomalous events extracted from monophonic collections of labelled audios. The performances of the proposed system have been evaluated through segment-based metrics such as error rate, recall, and F1-Score. Moreover, robustness and precision have been evaluated through four different tests. The analysis of the results shows that the proposed sound event detector outperforms both state-of-the-art methods and general purpose deep learning-solutions.

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Performance and Energy Balance: A Comprehensive Study of State-of-the-Art Sound Event Detection Systems

Ronchini,

Serizel

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

Cited by 8 publications

References 9 publications

A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

A Benchmark of State-of-the-Art Sound Event Detection Systems Evaluated on Synthetic Soundscapes

Sound Event Detection for Human Safety and Security in Noisy Environments

Performance and Energy Balance: A Comprehensive Study of State-of-the-Art Sound Event Detection Systems

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