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Found 5 papers, 2 papers with code
A General Framework for Learning Procedural Audio Models of Environmental Sounds
This paper introduces the Procedural (audio) Variational autoEncoder (ProVE) framework as a general approach to learning Procedural Audio PA models of environmental sounds with an improvement to the realism of the synthesis while maintaining provision of control over the generated sound through adjustable parameters.
Urban Rhapsody: Large-scale exploration of urban soundscapes
Noise is one of the primary quality-of-life issues in urban environments.
A Study on Robustness to Perturbations for Representations of Environmental Sound
To accomplish this, we imitate channel effects by injecting perturbations to the audio signal and measure the shift in the new (perturbed) embeddings with three distance measures, making the evaluation domain-dependent but not task-dependent.
Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes
While the estimation of what sound sources are, when they occur, and from where they originate has been well-studied, the estimation of how loud these sound sources are has been often overlooked.
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags.
