Sound Event Detection Using Point-Labeled Data

By Bongjun Kim, Bryan Pardo. 2019

14 citations recorded by the source corpus.

Sound Event Detection (SED) in audio scenes is the task that has been studied by an increasing number of researchers. Recent SED systems often use deep learning models. Building these systems typically require a large amount of carefully annotated, strongly labeled data, where the exact time-span of a sound event (e.g. the ‘dog bark’ starts at 1.2 seconds and ends at 2.0 seconds) in an audio scene (a recording of a city park) is indicated. However, manual labeling of sound events with their time boundaries within a recording is very time-consuming. One way to solve the issue is to collect data with weak labels that only contain the names of sound classes present in the audio file, without time boundary information for events in the file. Therefore, weakly-labeled sound event detection has become popular recently. However, there is still a large performance gap between models built on weakly labeled data and ones built on strongly labeled data, especially for predicting time boundaries of sound events. In this work, we introduce a new type of sound event label, which is easier for people to provide than strong labels. We call them ‘point labels’. To create a point label, a user simply listens to the recording and hits the space bar if they hear a sound event (’dog bark’). This is much easier to do than specifying exact time boundaries. In this work, we illustrate methods to train a SED model on point-labeled data. Our results show that a model trained on point labeled audio data significantly outperforms weak models and is comparable to a model trained on strongly labeled data.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “VGG-based architectures have been widely used for both SET [87, 118] and SED [119].”
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking (2020, background): “A similar masking approach to ignore false negatives in SER was recently used in [19].”
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer (2021, unknown): “, 2018) and localizing (McFee et al., 2018; Kim and Pardo, 2019) audio events.”