A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
By Eduardo Fonseca, Rong Gong, Xavier Serra. 2018
12 citations recorded by the source corpus.
Comunicacio presentada a: 15th Sound and Music Computing Conference (SMC2018). Sonic crossing, celebrat a Limassol, Xipre, del 4 al 7 de juliol de 2018.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Xavier Serra
- Eduardo Fonseca
- Frederic Font
- Xavier Favory
- Jordi Pons
- Manoj Plakal
- D. Ellis
- Andrés Ferraro
In Signal Processing
- Xavier Serra
- Eduardo Fonseca
- Frederic Font
- Xavier Favory
- Jordi Pons
- D. Ellis
- Manoj Plakal
- Andres Perez-Lopez
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “2) Network Architectures: Current trends in SER encompass mainly CNNs [87, 104, 112]–[114] and CRNNs [20, 115].”
- Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019, result): “It was proved beneficial for acoustic scene classification in [26], as well as in preliminary experiments with FSDnoisy18k.”
- A Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detection (2019, background): “5 is critical for overfitting mitigation, iii) more than one recurrent layer does not yield improvements, while slowing down training, and iv) surprisingly, slightly better performance is attained without BN nor pre-activation [24].”
- Model-Agnostic Approaches To Handling Noisy Labels When Training Sound Event Classifiers (2019, methodology): “The dataset’s baseline system is a CNN of 532k weights composed of three convolutional and one fully connected layers with pre-activation [25].”