116 citations · 125 across the 15 of their papers we have counts for
11 papers · 1 filter
SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds
Esteban Gutiérrez, Lonce Wyse, Frederic Font +1
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday sounds have become increasingly prohibitive, often requiring industri…
Architecture and Affordances of PLAUD: Performative Latents and Unsupervised DDSP
Błażej Kotowski, Frederic Font
PLAUD (Performative Latents and Unsupervised DDSP) is a neural synthesizer and Max for Live instrument for live electronic music, built on NoiseBandNet and trained on small persona…
Fractional Fourier Sound Synthesis
Esteban Gutiérrez, Rodrigo Cádiz, Carlos Sing Long +2
This paper explores the innovative application of the Fractional Fourier Transform (FrFT) in sound synthesis, highlighting its potential to redefine time-frequency analysis in audi…
A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
Esteban Gutiérrez, Frederic Font, Xavier Serra +1
In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds characterized by stochastic structure and perceptua…
Heterogeneous sound classification with the Broad Sound Taxonomy and Dataset
Panagiota Anastasopoulou, Jessica Torrey, Xavier Serra +1
Automatic sound classification has a wide range of applications in machine listening, enabling context-aware sound processing and understanding. This paper explores methodologies f…
Evaluating Neural Networks Architectures for Spring Reverb Modelling
Francesco Papaleo, Xavier Lizarraga-Seijas, Frederic Font
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 di…
FSD50K: An Open Dataset of Human-Labeled Sound Events
Eduardo Fonseca, Xavier Favory, Jordi Pons +2
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 an…
Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers
Eduardo Fonseca, Frederic Font, Xavier Serra
Label noise is emerging as a pressing issue in sound event classification. This arises as we move towards larger datasets that are difficult to annotate manually, but it is even mo…
Audio tagging with noisy labels and minimal supervision
Eduardo Fonseca, Manoj Plakal, Frederic Font +2
This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound…
Learning Sound Event Classifiers from Web Audio with Noisy Labels
Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis +3
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but…
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
Eduardo Fonseca, Manoj Plakal, Frederic Font +4
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platfo…