• Corpus ID: 220040070

TWO-STAGE SOUND EVENT LOCALIZATION AND DETECTION USING INTENSITY VECTOR AND GENERALIZED CROSS-CORRELATION Technical Report

@inproceedings{Cao2019TWOSTAGESE,
  title={TWO-STAGE SOUND EVENT LOCALIZATION AND DETECTION USING INTENSITY VECTOR AND GENERALIZED CROSS-CORRELATION Technical Report},
  author={Yin Cao and Turab Iqbal and Qiuqiang Kong and Miguel Galindo and Wenwu Wang and Mark D. Plumbley},
  year={2019}
}
Sound event localization and detection (SELD) refers to the spatial and temporal localization of sound events in addition to classification. The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Task 3 introduces a strongly labelled dataset to address this problem. In this report, a two-stage polyphonic sound event detection and localization method. The method utilizes log mel features for event detection, and uses intensity vector and GCC features for localization… 
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20 Citations
Highly Influential Citations
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Background Citations
4
Methods Citations
10
Results Citations
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20 Citations

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A new SELD method based on multiple direction of arrival (DOA) beamforming and multi-task learning, which achieves the state-of-art performance of DCASE2019 SELD, is proposed.

An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection

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Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs

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Sound Event Localization Based on Sound Intensity Vector Refined by Dnn-Based Denoising and Source Separation

The sound intensity vectors (IVs) for physics-based DOA estimation is refined based on DNN-based denoising and source separation, and this method enables the accurateDOA estimation for both single and overlapping sources using a spherical microphone array.

Sound Event Localization and Detection using CRNN Architecture with Mixup for Model Generalization

The proposed architecture is based on Convolutional-Recurrent Neural Network (CRNN) architecture and introduced rectangular kernels in the pooling layers to minimize the information loss in temporal dimension within the CNN module, leading to boosting up the RNN module performance.

Sound source detection, localization and classification using consecutive ensemble of CRNN models

This paper uses four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring events to decompose the SELD task into estimating number of active sources, estimating direction of arrival of a single source, estimating destination of the second source where the direction of the first one is known and a multi-label classification task.

TASK 3 DCASE 2020: SOUND EVENT LOCALIZATION AND DETECTION USING RESIDUAL SQUEEZE-EXCITATION CNNS Technical Report

This work aims to improve the accuracy results of the baseline CRNN by adding residual squeeze-excitation blocks in the convolutional part of the CRNN, and shows that by simply introducing the residual SE blocks, the results obtained in the development phase clearly exceed the baseline.

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