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Found 7 papers, 2 papers with code
Dataset balancing can hurt model performance
We find, however, that while balancing improves performance on the public AudioSet evaluation data it simultaneously hurts performance on an unpublished evaluation set collected under the same conditions.
Self-Supervised Learning from Automatically Separated Sound Scenes
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
For evaluation and semi-supervised experiments, we collected human labels for presence of on-screen and off-screen sounds on a small subset of clips.
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets.
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
Humans do not acquire perceptual abilities in the way we train machines.
Unsupervised Learning of Semantic Audio Representations
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds.
Ranked #41 on
Audio Classification
on AudioSet
CNN Architectures for Large-Scale Audio Classification
Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio.
