• 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

SOUND EVENT DETECTION AND LOCALIZATION USING CRNN MODELS Technical Report
TLDR
The Convolutional Recurrent Neural Network (CRNN) is developed that jointly predicts the Sound Event Detection (SED) and Degree of Arrival (DOA) hence minimizing the overlapping problems.
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection
TLDR
A two-step approach that decouples the learning of the sound event detection and directional-of-arrival estimation systems is proposed, which allows the flexibility in the system design, and increases the performance of the whole sound event localization and detection system.
SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection
TLDR
A novel feature called Spatial cue-Augmented Log-SpectrogrAm (SALSA) with exact time-frequency mapping between the signal power and the source directional cues, which is crucial for resolving overlapping sound sources is proposed.
Sound Event Localization and Detection Based on Multiple DOA Beamforming and Multi-Task Learning
TLDR
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
TLDR
The proposed EINV2 for joint SED and DoA estimation outperforms previous methods by a large margin, and has comparable performance to state-of-the-art ensemble models.
Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs
TLDR
This work aims to improve the accuracy results of the baseline CRNN presented in DCASE 2020 Task 3 by adding residual squeeze-excitation blocks in the convolutional part of the CRNN.
Sound Event Localization Based on Sound Intensity Vector Refined by Dnn-Based Denoising and Source Separation
TLDR
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
TLDR
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
TLDR
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
TLDR
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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