Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering
By Jean-Marc Valin, François Michaud, Jean Rouat. Robotics Auton. Syst., 2007
313 citations recorded by the source corpus.
Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 m. These new capabilities allow a mobile robot to interact using more natural means with people in real-life settings.
Subfields: Electrical and Electronic Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/dd56eba35973e359df9d67ad67336d5076bbdbdd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Signal Processing
- Tuomas Virtanen
- Sharath Adavanne
- Archontis Politis
- Joonas Nikunen
- Nikolaos Mavridis
- Eduardo Fonseca
- Xavier Serra
- Hakan Erdogan
Citations in the corpus
- Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks (2018, methodology): “The input sequence length is tuned in the set of [32 , 64 , 128 , 256 , 512] , the DOA and SED branch output loss weights in the set of [1 , 5 , 50 , 500] , the regularization (dropout in the set of [0 , 0 .”
- A review of verbal and non-verbal human-robot interactive communication (2014, methodology): “An approach utilizing scattering theory for sound source localization in robots is described in [187] and approaches using beamforming for multiple moving sources are presented in [188] and [189].”
- Self-Supervised Generation of Spatial Audio for 360 Video (2018, unknown): “However, unlike the proposed 360◦ spatialization problem, these works rely on microphone arrays using beamforming techniques [41] or binaural audio and HRTF cues similar to those used by humans [17].”