Detection and localization of impulsive sound events for environmental noise assessment

Weßels, P.; Sande, J. van de; Eerden, F. van der

doi:10.1121/1.4988711

Cited by 5 publications

(2 citation statements)

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“…Robots can employ this for navigation and natural interaction with surroundings [1][2][3][4]. Smart cities, smart homes, and industries could use it for audio surveillance [5][6][7][8]. Smart meeting rooms can recognize speech among other events and use this information to beamform and enhance the speech for teleconferencing or for robust automatic speech recognition [9][10][11][12][13].…”

mentioning

confidence: 99%

Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

Adavanne¹,

Politis²,

Nikunen³

et al. 2019

IEEE J. Sel. Top. Signal Process.

381

378

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In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in threedimensional (3D) space. The proposed network takes a sequence of consecutive spectrogram time-frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time-frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3D Cartesian coordinates of the direction-ofarrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method-and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.

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mentioning

confidence: 99%

Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

Adavanne¹,

Politis²,

Nikunen³

et al. 2019

IEEE J. Sel. Top. Signal Process.

381

378

View full text Add to dashboard Cite

show abstract

“…Applications such as teleconferencing systems and robots can use this information for tracking the sound event of interest [1][2][3][4][5][6]. Furthermore, smart cities and smart homes can use it for audio surveillance [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network

Adavanne¹,

Politis²,

Virtanen³

2019

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

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This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN). We use a CRNN previously proposed for the localization and detection of stationary sources, and show that the recurrent layers enable the spatial tracking of moving sources when trained with dynamic scenes. The tracking performance of the CRNN is compared with a stand-alone tracking method that combines a multisource (DOA) estimator and a particle filter. Their respective performance is evaluated in various acoustic conditions such as anechoic and reverberant scenarios, stationary and moving sources at several angular velocities, and with a varying number of overlapping sources. The results show that the CRNN manages to track multiple sources more consistently than the parametric method across acoustic scenarios, but at the cost of higher localization error.

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Sound Event Localization and Detection Using Parallel Multi-attention Enhancement

Chen,

Huang

2023

Circuits Syst Signal Process

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Detection and localization of impulsive sound events for environmental noise assessment

Cited by 5 publications

References 0 publications

Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network

Sound Event Localization and Detection Using Parallel Multi-attention Enhancement

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