Proceedings ArticleDOI
A neural network based algorithm for speaker localization in a multi-room environment
Fabio Vesperini,Paolo Vecchiotti,Emanuele Principi,Stefano Squartini,Francesco Piazza +4 more
- pp 1-6
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TLDR
A Speaker Localization algorithm based on Neural Networks for multi-room domestic scenarios is proposed and outperforms the reference one, providing an average localization error, expressed in terms of RMSE, equal to 525 mm against 1465 mm.Abstract:
A Speaker Localization algorithm based on Neural Networks for multi-room domestic scenarios is proposed in this paper. The approach is fully data-driven and employs a Neural Network fed by GCC-PHAT (Generalized Cross Correlation Phase Transform) Patterns, calculated by means of the microphone signals, to determine the speaker position in the room under analysis. In particular, we deal with a multi-room case study, in which the acoustic scene of each room is influenced by sounds emitted in the other rooms. The algorithm is tested against the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room. In particular, we focused on the speaker localization problem in two distinct neighbouring rooms. We assumed the presence of an Oracle multi-room Voice Activity Detector (VAD) in our experiments. A three-stage optimization procedure has been adopted to find the best network configuration and GCC-PHAT Patterns combination. Moreover, an algorithm based on Time Difference of Arrival (TDOA), recently proposed in literature for the addressed applicative context, has been considered as term of comparison. As result, the proposed algorithm outperforms the reference one, providing an average localization error, expressed in terms of RMSE, equal to 525 mm against 1465 mm. Concluding, we also assessed the algorithm performance when a real VAD, recently proposed by some of the authors, is used. Even though a degradation of localization capability is registered (an average RMSE equal to 770 mm), still a remarkable improvement with respect to the state of the art performance is obtained.read more
Citations
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Journal ArticleDOI
Deep Learning for Audio Signal Processing
TL;DR: Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas.
Journal ArticleDOI
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
TL;DR: The proposed convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios.
Journal ArticleDOI
Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections
TL;DR: A framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios andSimulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.
Journal ArticleDOI
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained With Noise Signals
TL;DR: The ability of the proposed convolutional neural network based supervised learning method for estimating the direction of arrival (DOA) of multiple speakers to adapt to unseen acoustic conditions and its robustness to unseen noise type is demonstrated.
Proceedings ArticleDOI
Direction of Arrival Estimation for Multiple Sound Sources Using Convolutional Recurrent Neural Network
TL;DR: In this paper, a deep neural network was proposed to estimate the directions of arrival (DOA) of multiple sound sources in anechoic, matched and unmatched reverberant conditions.
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