A multi-room reverberant dataset for sound event localization and detection
@inproceedings{Adavanne2019AMR, title={A multi-room reverberant dataset for sound event localization and detection}, author={Sharath Adavanne and Archontis Politis and Tuomas Virtanen}, booktitle={DCASE}, year={2019} }
This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. [] Key Method These sound events are spatialized using real-life impulse responses collected at multiple spatial coordinates in five different rooms with varying dimensions and material properties. A baseline SELD method employing a convolutional recurrent neural network is used to generate benchmark scores for this reverberant dataset. The benchmark scores are obtained using the recommended cross…
57 Citations
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection
- Computer ScienceDCASE
- 2021
To investigate the individual and combined effects of ambient noise, interferers, and reverberation, the performance of the baseline on different versions of the dataset excluding or including combinations of these factors indicates that by far the most detrimental effects are caused by directional interferers.
SOUND EVENT DETECTION AND LOCALIZATION USING CRNN MODELS Technical Report
- Computer Science, Physics
- 2020
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.
SECL-UMons Database for Sound Event Classification and Localization
- Computer ScienceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2020
The DCASE 2019 challenge baseline (SELDnet) employing a convolutional recurrent neural network is used to generate benchmark scores for the new SECL-UMons dataset for sound event classification and localization in the context of office environments.
SOUND EVENT LOCALIZATION AND DETECTION USING FOA DOMAIN SPATIAL AUGMENTATION Technical Report
- Computer Science
- 2019
The proposed spatial augmentation enables the system participating to the DCASE 2019, Task 3: Sound Event Localization and Detection challenge to augment direction of arrival (DOA) labels without losing physical relationships between steering vectors and observations.
Sound source detection, localization and classification using consecutive ensemble of CRNN models
- Computer ScienceDCASE
- 2019
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.
Metric optimization for Sound Event Localization and Detection
- Computer Science, Physics2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)
- 2020
Three methods are proposed: soft f-loss with temporal masking, periodic loss, and PoolNet-based architecture to handle three issues of problem with dataset imbalance, pooling size decision, and periodicity of angles.
Sound Event Localization and Detection Using CRNN on Pairs of Microphones
- Computer ScienceDCASE
- 2019
This paper proposes sound event localization and detection methods from multichannel recording based on two Convolutional Recurrent Neural Networks to perform sound event detection (SED) and time difference of arrival (TDOA) estimation on each pair of microphones in a microphone array.
A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection
- Computer ScienceICASSP
- 2022
A trackwise ensemble event independent network with a novel data augmentation method based on the previous proposed Event-Independent Network V2 and extended by conformer blocks and dense blocks is proposed to solve an ensemble model problem for track-wise output format that track permutation may occur among different models.
DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection
- Computer Science, PhysicsArXiv
- 2021
This work proposes a novel feature called spatial cue-augmented log-spectrogram (SALSA) with exact time-frequency mapping between the signal power and the source direction-of-arrival, and combined several models with slightly different architectures that were trained on the new feature to further improve the system performances for the DCASE sound event localization and detection challenge.
A Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detection
- Computer ScienceDCASE
- 2019
The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier, to reduce the localization error on the evaluation dataset.
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