Method for tracking a target acoustic source

Patent metadata

Field	Value
Publication number	US-10094911-B2
Application number	US-201515531444-A
Country	US
Kind code	B2
Filing date	Nov 18, 2015
Priority date	Dec 2, 2014
Publication date	Oct 9, 2018
Grant date	Oct 9, 2018

Abstract

A method of processing an acoustic image includes the steps of acquiring acoustic signals generated by acoustic sources in a predetermined region of space, generating a multispectral 3D acoustic image that includes a collection of 2D acoustic images, performing a frequency integration of the multispectral acoustic image for generating a 2D acoustic map, locating at least one target acoustic source of interest and modeling the signal spectrum associated with the target acoustic source, generating a classification map obtained by comparing the signal spectrum of each signal associated with each pixel of the multispectral acoustic image and the model of the signal spectrum associated with the target acoustic source to distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with the remaining acoustic sources, and merging the classification map and the acoustic map to obtain a merged map.

First claim

The invention claimed is:

1. A method of processing an acoustic image comprising the following steps:
a) acquiring acoustic signals generated by acoustic sources in a predetermined region of space;
b) generating a multispectral 3D acoustic image ( 1 ), consisting of a collection of 2D acoustic images, each 2D acoustic image being formed by transposition of a position of each of the acquired acoustic sources into a grayscale or color model, each 2D acoustic image being identified by a single frequency or a frequency band, such that each 2D acoustic image has the position of each of the acquired audio sources marked thereon along axes of coordinates of the 2D acoustic image, for spatial allocation of the acquired acoustic sources;
c) performing a frequency integration of said multispectral 3D acoustic image for generating a 2D acoustic map;
d) locating at least one target acoustic source of interest and modeling a signal spectrum associated with said target acoustic source;
e) generating a classification map obtained by comparing the signal spectrum of each signal associated with each pixel of said multispectral acoustic image and a model of the signal spectrum associated with said target acoustic source, the step of comparing being obtained by training a classification algorithm, said classification algorithm being executed for each pixel of said multispectral acoustic image, to thereby distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with remaining acoustic sources; and
f) merging said classification map and said acoustic map to obtain a merged map.

2. The method as claimed in claim 1 , wherein step d) comprises the following step:
d1) identifying a spectral signature of the signal generated by the target acoustic source, and
wherein step e) comprises the following steps:
e1) comparing the spectral signature of the signal generated by the target acoustic source with the spectral signatures of the signals associated with the individual pixels of said multispectral acoustic image ( 1 ), and
e2) generating said classification map, such that a value of each pixel of said classification map indicates a probability that each signal being compared will be transmitted by the target acoustic source.

3. The method as claimed in claim 1 , wherein step d) comprises a sub-step d2) of identifying spectral signatures of the signals generated by acoustic noise sources, the classification algorithm being trained to distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with the acoustic noise sources.

4. The method as claimed in claim 1 , wherein step f) comprises multiplying values of the pixels of the acoustic map obtained in step c) by the values of the pixels of the classification map obtained in step e).

5. The method as claimed in claim 1 , wherein step c) comprises weighting the frequencies or frequency bands of said multispectral acoustic image.

6. The method as claimed in claim 1 , wherein said method comprises tracking the target acoustic source with the following steps:
g) generating a probability function based on said merged map, and
h) executing a tracking algorithm.

7. The method as claimed in claim 1 , wherein step g) comprises the following steps:
g1) transforming the merged map into a probability function,
g2) generating an additional probability function indicating a possible dynamic path of the target acoustic source, obtained using predetermined dynamic models, and
g3) comparing the probability function obtained in step g1) and the additional probability function generated in step g2) to express a conditional probability that said merged map ( 4 ) has been obtained using a dynamic model of the target acoustic source.

8. The method as claimed in claim 1 , wherein step a) is carried out using an array of acoustic sensors and comprises a substep a1) of calibrating said array of acoustic sensors.

9. The method as claimed in claim 8 , wherein said substep a1) comprises acquiring an optical image acquired with a camera or videocamera device,
further comprising a step c1) superimposing the acoustic map generated in step c) on the acquired optical image.

10. The method as claimed in claim 6 , wherein steps a) to h) are carried out in real-time mode.

Assignees

• Fondazione St Italiano Tecnologia

Inventors

• Crocco Marco
• Murino Vittorio
• Trucco Andrea
• Martelli Samuele

Classifications

• G06F2218/16

  by matching signal segments · CPC title

• G06T11/26

  Drawing of charts or graphs · CPC title

• G01S7/539

  using analysis of echo signal for target characterisation; Target signature; Target cross-section · CPC title

• G01S3/8006

  Multi-channel systems specially adapted for direction-finding, i.e. having a single aerial system capable of giving simultaneous indications of the directions of different signals · CPC title

• G06F18/2148

  characterised by the process organisation or structure, e.g. boosting cascade · CPC title