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Found 15 papers, 8 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while balancing improves performance on the public AudioSet evaluation data it simultaneously hurts performance on an unpublished evaluation set collected under the same conditions.
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset.
MuLan: A Joint Embedding of Music Audio and Natural Language
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
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.
Improving Universal Sound Separation Using Sound Classification
Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification.
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
Humans do not acquire perceptual abilities in the way we train machines.
Audio tagging with noisy labels and minimal supervision
The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes.
Learning Sound Event Classifiers from Web Audio with Noisy Labels
To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42. 5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.
AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies
Speech activity detection (or endpointing) is an important processing step for applications such as speech recognition, language identification and speaker diarization.
Sound Audio and Speech Processing
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
The goal of the task is to build an audio tagging system that can recognize the category of an audio clip from a subset of 41 diverse categories drawn from the AudioSet Ontology.
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.
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems
We propose a simplified model of attention which is applicable to feed-forward neural networks and demonstrate that the resulting model can solve the synthetic "addition" and "multiplication" long-term memory problems for sequence lengths which are both longer and more widely varying than the best published results for these tasks.
