A neural network based microphone array approach to grid-less noise source localization

Castellini, Paolo; Giulietti, Nicola; Falcionelli, Nicola; Dragoni, Aldo Franco; Chiariotti, Paolo

doi:10.1016/j.apacoust.2021.107947

Cited by 23 publications

(4 citation statements)

References 13 publications

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“…Recent work also explored distributed microphone arrays 1 . However, they did not satisfy the above goals: they were evaluated in simulated or strongly constrained environments [22][23][24][25] , required exact microphone positions [26][27][28][29] , used wired setups to achieve synchronization 26,30,31 , localized only 1-2 speakers [31][32][33][34][35] , or assumed a priori knowledge about the number of speakers [36][37][38] .…”

Section: Speech Separation and 2d Localizationmentioning

confidence: 99%

Creating speech zones with self-distributing acoustic swarms

Itani,

Chen,

Yoshioka

et al. 2023

Nat Commun

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Imagine being in a crowded room with a cacophony of speakers and having the ability to focus on or remove speech from a specific 2D region. This would require understanding and manipulating an acoustic scene, isolating each speaker, and associating a 2D spatial context with each constituent speech. However, separating speech from a large number of concurrent speakers in a room into individual streams and identifying their precise 2D locations is challenging, even for the human brain. Here, we present the first acoustic swarm that demonstrates cooperative navigation with centimeter-resolution using sound, eliminating the need for cameras or external infrastructure. Our acoustic swarm forms a self-distributing wireless microphone array, which, along with our attention-based neural network framework, lets us separate and localize concurrent human speakers in the 2D space, enabling speech zones. Our evaluations showed that the acoustic swarm could localize and separate 3-5 concurrent speech sources in real-world unseen reverberant environments with median and 90-percentile 2D errors of 15 cm and 50 cm, respectively. Our system enables applications like mute zones (parts of the room where sounds are muted), active zones (regions where sounds are captured), multi-conversation separation and location-aware interaction.

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Section: Speech Separation and 2d Localizationmentioning

confidence: 99%

Creating speech zones with self-distributing acoustic swarms

Itani,

Chen,

Yoshioka

et al. 2023

Nat Commun

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“…An MLP network is exploited to perform a binary classification of the collected spectra. MLP has been used for a long time in numerous scientific and technological sectors and has proven to be able to solve very complex problems in several fields [ 17 ]. This model architecture is able to predict new continuous outputs from new statistically independent input data starting from a set of continuous input–output variables.…”

Section: Image Processing and Data Collectionmentioning

confidence: 99%

Correction of Substrate Spectral Distortion in Hyper-Spectral Imaging by Neural Network for Blood Stain Characterization

Giulietti

Discepolo

Castellini

et al. 2022

Sensors

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In the recent past, hyper-spectral imaging has found widespread application in forensic science, performing both geometric characterization of biological traces and trace classification by exploiting their spectral emission. Methods proposed in the literature for blood stain analysis have been shown to be effectively limited to collaborative surfaces. This proves to be restrictive in real-case scenarios. The problem of the substrate material and color is then still an open issue for blood stain analysis. This paper presents a novel method for blood spectra correction when contaminated by the influence of the substrate, exploiting a neural network-based approach. Blood stains hyper-spectral images deposited on 12 different substrates for 12 days at regular intervals were acquired via a hyper-spectral camera. The data collected were used to train and test the developed neural network model. Starting from the spectra of a blood stain deposited in a generic substrate, the algorithm at first recognizes whether it is blood or not, then allows to obtain the spectra that the same blood stain, at the same time, would have on a reference white substrate with a mean absolute percentage error of 1.11%. Uncertainty analysis has also been performed by comparing the ground truth reflectance spectra with the predicted ones by the neural model.

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“…The microphone array sound source location technology uses the microphone array to collect sound signals and then realizes the sound source location through the sound source location algorithm. 2 Nowadays, most scholars use the linear microphone array or planar microphone array to collect sound signals and combine or improve the maximum output power controllable beamforming, high-resolution spectrum estimation, and time-delay estimation sound source localization algorithms to achieve sound source localization. Zhang Kun et al 3 used a five-element linear microphone array to receive the sound signal.…”

Section: Introductionmentioning

confidence: 99%

Sound source localization model of cube microphone array

Wang

Tian

2023

Adv Control Appl

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This paper conducts in‐depth research on the sound source localization technology of the microphone array and designs a sound source localization system based on the cube microphone array. Firstly, the sound source localization model is established using the cube microphone array combined with the spherical near‐field acoustic holography. Secondly, the numerical and sound source localization simulations are carried out using the spherical wave. Finally, the simulation and experiment of sound source location for sound at 100, 1000, and 2000 Hz are carried out using the model. Both simulation and experimental results show that when the sound source frequency is 100 and 1000 Hz, the location of the sound source can be accurately located by using the sound source localization model of a cube microphone array, and the sound field reconstruction error is low. When the sound source frequency is 2000 Hz, the location of the sound source cannot be located, and the sound field reconstruction error is very high, which will cause the misjudgment of the sound source location.

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A neural network based microphone array approach to grid-less noise source localization

Cited by 23 publications

References 13 publications

Creating speech zones with self-distributing acoustic swarms

Creating speech zones with self-distributing acoustic swarms

Correction of Substrate Spectral Distortion in Hyper-Spectral Imaging by Neural Network for Blood Stain Characterization

Sound source localization model of cube microphone array

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