[PDF][PDF] Interpreting and explaining deep neural networks for classification of audio signals
Interpretability of deep neural networks is a recently emerging area of machine learning
research targeting a better understanding of how models perform feature selection and�…
research targeting a better understanding of how models perform feature selection and�…
Learning environmental sounds with multi-scale convolutional neural network
B Zhu, C Wang, F Liu, J Lei, Z Huang…�- …�joint conference on�…, 2018 - ieeexplore.ieee.org
Deep learning has dramatically improved the performance of sounds recognition. However,
learning acoustic models directly from the raw waveform is still challenging. Current�…
learning acoustic models directly from the raw waveform is still challenging. Current�…
[PDF][PDF] CP-JKU submissions to DCASE'19: Acoustic scene classification and audio tagging with receptive-field-regularized CNNs
In this report, we detail the CP-JKU submissions to the DCASE-2019 challenge Task 1
(acoustic scene classification) and Task 2 (audio tagging with noisy labels and minimal�…
(acoustic scene classification) and Task 2 (audio tagging with noisy labels and minimal�…
[HTML][HTML] SampleCNN: End-to-end deep convolutional neural networks using very small filters for music classification
Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw�…
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw�…
[HTML][HTML] Data augmentation and deep learning methods in sound classification: A systematic review
The aim of this systematic literature review (SLR) is to identify and critically evaluate current
research advancements with respect to small data and the use of data augmentation�…
research advancements with respect to small data and the use of data augmentation�…
Wavenet: A generative model for raw audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each�…
The model is fully probabilistic and autoregressive, with the predictive distribution for each�…
[PDF][PDF] Wavenet: A generative model for raw audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each�…
The model is fully probabilistic and autoregressive, with the predictive distribution for each�…
Data augmentation using generative adversarial network for environmental sound classification
A Madhu, S Kumaraswamy�- 2019 27th European signal�…, 2019 - ieeexplore.ieee.org
Various types of deep learning architecture have been steadily gaining impetus for
automatic environmental sound classification. However, the relative paucity of publicly�…
automatic environmental sound classification. However, the relative paucity of publicly�…
Environment sound classification using multiple feature channels and attention based deep convolutional neural network
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that
consists of multiple feature channels given as input to a Deep Convolutional Neural Network�…
consists of multiple feature channels given as input to a Deep Convolutional Neural Network�…
Environment sound classification using an attention-based residual neural network
AM Tripathi, A Mishra�- Neurocomputing, 2021 - Elsevier
Complexity of environmental sounds impose numerous challenges for their classification.
The performance of Environmental Sound Classification (ESC) depends greatly on how�…
The performance of Environmental Sound Classification (ESC) depends greatly on how�…