Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking

Abstract

The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels, one of the big weaknesses of large audio datasets, and one of the most conspicuous issues for AudioSet. We propose a simple and model-agnostic method based on a teacher-student framework with loss masking to first identify the most critical missing label candidates, and then ignore their contribution during the learning process. We find that a simple optimisation of the training label set improves recognition performance without additional computation. We discover that most of the improvement comes from ignoring a critical tiny portion of the missing labels. We also show that the damage done by missing labels is larger as the training set gets smaller, yet it can still be observed even when training with massive amounts of audio. We believe these insights can generalize to other large-scale datasets.

Accepted in IEEE Signal Processing Letters, openly accessible at https://ieeexplore.ieee.org/document/9130823

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Distilling the Knowledge in a Neural Network
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Do Deep Nets Really Need to be Deep?
• Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
• General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
• PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
• A Closer Look at Weak Label Learning for Audio Events

Cited by in corpus (5)

• Sound Event Detection: A Tutorial
• PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation
• Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition
• Ontology-aware Learning and Evaluation for Audio Tagging
• Enriching Ontology with Temporal Commonsense for Low-Resource Audio Tagging