Robust detection of overlapping bioacoustic sound events
We propose a method for accurately detecting bioacoustic sound events that is robust to overlapping events, a common issue in domains such as ethology, ecology and conservation. While standard methods employ a frame-based, multi-label approach, we introduce an onset-based detection method which we name Voxaboxen. It takes inspiration from object detection methods in computer vision, but simultaneously takes advantage of recent advances in self-supervised audio encoders. For each time window, Voxaboxen predicts whether it contains the start of a vocalization and how long the vocalization is. It also does the same in reverse, predicting whether each window contains the end of a vocalization, and how long ago it started. The two resulting sets of bounding boxes are then fused using a graph-matching algorithm. We also release a new dataset designed to measure performance on detecting overlapping vocalizations. This consists of recordings of zebra finches annotated with temporally-strong labels and showing frequent overlaps. We test Voxaboxen on seven existing data sets and on our new data set. We compare Voxaboxen to natural baselines and existing sound event detection methods and demonstrate SotA results. Further experiments show that improvements are robust to frequent vocalization overlap.
Code (0)
등록된 구현이 없습니다.
Tasks
Event DetectionGraph Matchingobject-detectionObject DetectionSound Event DetectionSimilar Papers 제목 키워드 기반
Few-shot bioacoustic event detection at the DCASE 2022 challenge
Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a ne…
Event DetectionSound Event DetectionTransductive LearningRegularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquir…
Event DetectionFew-Shot LearningSound Event DetectionPretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with corresponding annotations when supervised …
Contrastive LearningData AugmentationEvent DetectionFew-Shot Learning+1Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning
The vast amounts of audio data collected in Sound Event Detection (SED) applications require efficient annotation strategies to enable supervised learning. Manual labeling is expensive and time-consuming, making Active L…
Active LearningEvent DetectionSound Event DetectionSegment-level Metric Learning for Few-shot Bioacoustic Event Detection
Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different so…
Event DetectionFew-Shot LearningMetric LearningModel Optimization