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Found 11 papers, 8 papers with code
The language of sound search: Examining User Queries in Audio Search Engines
These insights are crucial for developing user-centred, effective text-based audio retrieval systems, enhancing our understanding of user behaviour in sound search contexts.
Heterogeneous sound classification with the Broad Sound Taxonomy and Dataset
We compare a variety of both traditional and modern machine learning approaches to establish a baseline for the task of heterogeneous sound classification.
Evaluating Neural Networks Architectures for Spring Reverb Modelling
Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM).
FSD50K: An Open Dataset of Human-Labeled Sound Events
Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2M tracks from YouTube videos and encompassing over 500 sound classes.
The Freesound Loop Dataset and Annotation Tool
We present the Freesound Loop Dataset (FSLD), a new large-scale dataset of music loops annotated by experts.
Audio and Speech Processing Sound
Search Result Clustering in Collaborative Sound Collections
In our work, we propose a graph-based approach using audio features for clustering diverse sound collections obtained when querying large online databases.
Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers
We show that these simple methods can be effective in mitigating the effect of label noise, providing up to 2. 5\% of accuracy boost when incorporated to two different CNNs, while requiring minimal intervention and computational overhead.
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.
Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections.
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.
