Waveform-based End-to-end Deep Convolutional Neural Network with Multi-scale Sliding Windows for Weakly Labeled Sound Event Detection

Lee, Seokjin; Kim, Minhan

doi:10.1109/icaiic48513.2020.9064985

Cited by 3 publications

(2 citation statements)

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“…In the systems proposed for DCASE 2018 and 2019 Challenge Task 4, the value for this threshold is usually set to th = 0.5, without further justification for choosing such value. Nevertheless, some different thresholding strategies have been proposed, such as double-thresholding [39] or dynamic thresholding [40].…”

Section: Existing Approaches To Sound Event Detectionmentioning

confidence: 99%

A Multi-Resolution CRNN-Based Approach for Semi-Supervised Sound Event Detection in DCASE 2020 Challenge

2021

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Sound Event Detection is a task with a rising relevance over the recent years in the field of audio signal processing, due to the creation of specific datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and the introduction of competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). The different categories of acoustic events can present diverse temporal and spectral characteristics. However, most approaches use a fixed time-frequency resolution to represent the audio segments. This work proposes a multi-resolution analysis for feature extraction in Sound Event Detection, hypothesizing that different resolutions can be more adequate for the detection of different sound event categories, and that combining the information provided by multiple resolutions could improve the performance of Sound Event Detection systems. Experiments are carried out over the DESED dataset in the context of the DCASE 2020 Challenge, concluding that the combination of up to 5 resolutions allows a neural network-based system to obtain better results than single-resolution models in terms of event-based F1-score in every event category and in terms of PSDS (Polyphonic Sound Detection Score). Furthermore, we analyze the impact of score thresholding in the computation of F1-score results, finding that the standard value of 0.5 is suboptimal and proposing an alternative strategy based in the use of a specific threshold for each event category, which obtains further improvements in performance.

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Section: Existing Approaches To Sound Event Detectionmentioning

confidence: 99%

A Multi-Resolution CRNN-Based Approach for Semi-Supervised Sound Event Detection in DCASE 2020 Challenge

2021

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“…By GLUs, the network can focus on sound events and ignore irrelevant sounds. In [22], bidirectional long short-term memory (BLSTM) was applied to weakly labeled learning. Since weakly labeled data was used, accurate error calculation cannot be performed to update the parameters of BLSTM.…”

Section: Introductionmentioning

confidence: 99%

Improved capsule routing for weakly labeled sound event detection

Yang

Wang

2022

J AUDIO SPEECH MUSIC PROC.

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Polyphonic sound event detection aims to detect the types of sound events that occur in given audio clips, and their onset and offset times, in which multiple sound events may occur simultaneously. Deep learning–based methods such as convolutional neural networks (CNN) achieved state-of-the-art results in polyphonic sound event detection. However, two open challenges still remain: overlap between events and prone to overfitting problem. To solve the above two problems, we proposed a capsule network-based method for polyphonic sound event detection. With so-called dynamic routing, capsule networks have the advantage of handling overlapping objects and the generalization ability to reduce overfitting. However, dynamic routing also greatly slows down the training process. In order to speed up the training process, we propose a weakly labeled polyphonic sound event detection model based on the improved capsule routing. Our proposed method is evaluated on task 4 of the DCASE 2017 challenge and compared with several baselines, demonstrating competitive results in terms of F-score and computational efficiency.

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