Heavily Augmented Sound Event Detection utilizing Weak Predictions

07/08/2021
by   Hyeonuk Nam, et al.
0

The performances of Sound Event Detection (SED) systems are greatly limited by the difficulty in generating large strongly labeled dataset. In this work, we used two main approaches to overcome the lack of strongly labeled data. First, we applied heavy data augmentation on input features. Data augmentation methods used include not only conventional methods used in speech/audio domains but also our proposed method named FilterAugment. Second, we propose two methods to utilize weak predictions to enhance weakly supervised SED performance. As a result, we obtained the best PSDS1 of 0.4336 and best PSDS2 of 0.8161 on the DESED real validation dataset. This work is submitted to DCASE 2021 Task4 and is ranked on the 3rd place. Code availa-ble: https://github.com/frednam93/FilterAugSED.

READ FULL TEXT
01/19/2021

Towards duration robust weakly supervised sound event detection

Sound event detection (SED) is the task of tagging the absence or presen...
07/21/2022

Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learning

Few-shot audio event detection is a task that detects the occurrence tim...
03/19/2022

A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection

Polyphonic sound event localization and detection (SELD) aims at detecti...

Code Repositories

Please sign up or login with your details

Forgot password? Click here to reset