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Found 6 papers, 4 papers with code
Representation Learning for Audio Privacy Preservation using Source Separation and Robust Adversarial Learning
Privacy preservation has long been a concern in smart acoustic monitoring systems, where speech can be passively recorded along with a target signal in the system's operating environment.
Adversarial Representation Learning for Robust Privacy Preservation in Audio
In this study, we propose a novel adversarial training method for learning representations of audio recordings that effectively prevents the detection of speech activity from the latent features of the recordings.
Sound Event Detection with Depthwise Separable and Dilated Convolutions
The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions.
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
On the contrary, with our method there is a decrease of 4% at F1 score and an increase of 7% at ER for the TUT-SED Synthetic 2016 dataset.
Unsupervised adversarial domain adaptation for acoustic scene classification
A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy.
Acoustic Scene Classification: A Competition Review
In this paper we study the problem of acoustic scene classification, i. e., categorization of audio sequences into mutually exclusive classes based on their spectral content.
