Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

@inproceedings{Plumbley2016ProceedingsOT,
  title={Proceedings of the Detection and Classification of Acoustic Scenes and
 Events 2019 Workshop (DCASE2019)},
  author={Mark D. Plumbley and Christian Kroos and Juan Pablo Bello and Ga{\"e}l Richard and Daniel P. W. Ellis and Annamaria Mesaros},
  year={2016}
}
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of overlapping sound events using pitch cues and exploiting the stereo (multichannel) audio signal available… 
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29 Citations
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Methods Citations
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Results Citations
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29 Citations

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Acoustic Scene Classification Using Deep Audio Feature and BLSTM Network
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This work presents the work of acoustic scene classification for the challenge of the Detection and Classification of Acoustic Scenes and Events 2017, i.e., DCASE2017 challenge, using a feature of Deep Audio Feature (DAF) for acoustic scene representation and a classifier of Bidirectional Long Short Term Memory (BLSTM) network foroustic scene classification.
Sound Event Detection Using Multiple Optimized Kernels
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Experimental results on different subsets of AudioSet demonstrate the performance of the proposed approach compared to state-of-the-art systems.
GCC-PHAT Cross-Correlation Audio Features for Simultaneous Sound Event Localization and Detection (SELD) on Multiple Rooms
In this work, we show a simultaneous sound event localization and detection (SELD) system, with enhanced acoustic features, in which we propose using the well-known Generalized Cross Correlation…
An Xception Residual Recurrent Neural Network for Audio Event Detection and Tagging
TLDR
The Xception Stacked Residual Recurrent Neural Network (XRRNN) is proposed, based on modifications of the system CVSSP by Xu et al. (2017), that won the challenge for the AT task.
Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification
  • Yuzhong Wu, Tan Lee
  • Physics
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
TLDR
Analysis of detailed experimental results reveals that (1) long-duration sounds are generally most informative for acoustic scene classification; and (2) the focus of sound duration may be different for classifying different types of acoustic scenes.
Acoustic Scene Classification Using Audio Tagging
TLDR
A novel scheme for acoustic scene classification which adopts an audio tagging system inspired by the human perception mechanism, which shows effectiveness on the detection and classification of acoustic scenes and events 2019 task 1-a dataset.
Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events
TLDR
This work modify this attention-based feedforward structure in such a way that allows the resulting model to use audio as well as video to compute sound event predictions, and makes a compelling case for devoting more attention to research in multimodal audiovisual classification.
Audio Sound Determination Using Feature Space Attention Based Convolution Recurrent Neural Network
TLDR
Improved performance on the latest TUT Sound Event 2017 dataset demonstrate the improved performance of the proposed feature space attention based convolution recurrent neural network approach utilizing the varying importance of each feature dimension to perform acoustic event detection.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
TLDR
This study proposes an integrated deep neural network that can perform three tasks: acoustic scene classification, audio tagging, and sound event detection and shows that the proposed system, DCASENet, itself can be directly used for any tasks with competitive results, or it can be further finetuned for the target task.
Progressive Training Of Convolutional Neural Networks For Acoustic Events Classification
TLDR
Experimental results suggest that progressive resizing methods improves the performances of audio events classification models as well as introducing a complimentary gain in performances with respect to the original technique.
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