Proposal and validation of an analytical generative model of SRP-PHAT power maps in reverberant scenarios

Velasco, Jose; Martín-Arguedas, Carlos J.; Macías-Guarasa, Javier; Pizarro, Daniel; Mazo, Manuel

doi:10.1016/j.sigpro.2015.08.003

Cited by 22 publications

(36 citation statements)

References 33 publications

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“…[ 60 ], which can be difficult to acquire for general positions in big environments, or by using room response simulation methods, such as the image method [ 64 ] used in Ref. [ 65 ] for this purpose. The acoustic noise conditions of the room and the recording process conditions: These can result from additional equipment (computers, fans, air conditioning systems, etc.)…”

Section: System Descriptionmentioning

confidence: 99%

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

Vera-Diaz

Pizarro

Macías-Guarasa

2018

Sensors

Self Cite

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This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker’s gender, nor on the size of the signal window being used.

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Section: System Descriptionmentioning

confidence: 99%

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

Vera-Diaz

Pizarro

Macías-Guarasa

2018

Sensors

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“…In [98] the performance of SRP-PHAT is analyzed over a massive multichannel processing framework in a multi-GPU system, analyzing its performance as a function of the number of microphones and available computational resources in the system. Note, however, that the performance of SRP approaches is also related to the properties of the sound sources, such as their bandwidth or their low-pass/pass-band nature [99,100].…”

Section: Modified Srp-phat (M-srp)mentioning

confidence: 99%

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Cobos

Antonacci

Alexandridis

et al. 2017

Wireless Communications and Mobile Computing

114

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Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field.

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“…Different alternatives can be used to simulate this effect, such as convolving the anechoic signals with real room impulse responses such as in [47], that can be difficult to acquire for general positions in big environments; or using room response simulation methods such as the image method [61] used in [62] for this purpose. • Acoustic noise conditions of the room and recording process conditions: These can be due to additional equipment (computers, fans, air conditioning systems, etc.)…”

Section: Semi-synthetic Dataset Generationmentioning

confidence: 99%

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signal to Source Position Coordinates

Vera-Diaz¹,

Pizarro²,

Macías-Guarasa³

2018

Preprint

Self Cite

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This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.

show abstract

Proposal and validation of an analytical generative model of SRP-PHAT power maps in reverberant scenarios

Cited by 22 publications

References 33 publications

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signal to Source Position Coordinates

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