Reliable detection of audio events in highly noisy environments
By Pasquale Foggia, N. Petkov, Alessia Saggese, Nicola Strisciuglio, Mario Vento. 2015
170 citations recorded by the source corpus.
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
- Eduardo Fonseca
- Xavier Serra
- Weidi Xie
- Andrew Zisserman
- Tuomas Virtanen
- Andrea Vedaldi
- Luc Van Gool
- Ming-Hsuan Yang
In Signal Processing
- Eduardo Fonseca
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Tuomas Virtanen
- Andrea Vedaldi
- Toni Heittola
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “Early stage works in SER relied on feature engineering approaches using standard machine learning classifiers such as support vector machines [14], Gaussian mixture models [15] or matrix factorization techniques [16].”
- Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017, unknown): “SED has been drawing a surging amount of interest in recent years with applications including audio surveillance [1], healthcare monitoring [2], urban sound analysis [3], multimedia event detection [4] and bird call detection [5].”
- Metrics for Polyphonic Sound Event Detection (2016, background): “Studies involving both monophonic and polyphonic sound event detection report results using a variety of metrics, for example Precision, Recall and F-score [6] or only F-score [7,26], recognition rate and false positive rate [3], or false positive and false negative rates [1].”
- Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments (2016, background): “Yet, it has a wide range of applications such as multimedia data search, context awareness and activity detection [1–4], security surveillance [5,6], military interest tracking [7], assistive devices for independent living [8], healthcare monitoring [9,10], among others.”
- Vggsound: A Large-Scale Audio-Visual Dataset (2020, methodology): “The Mivia Audio Events Dataset [7] focuses on surveillance applications and contains 6 k audio clips for 3 classes.”