A Comparative Approach of Blind Source Separation with Arduino Due and TMS320C6713

Mekhfioui, Mohcin

doi:10.30534/ijeter/2020/42832020

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The paper highlighted current trends, difficulties and future opportunities in the given area. One of the early FPGA implementations for real-time sound localization was presented in [34].A system for blind source separation from a mixture of multi-channel data has been implemented in [35].…”

Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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Microphone arrays are commonly used for Direction of Arrival (DOA) estimation of audio sources. This paper presents the design and implementation of a real-time embedded system (RTES) for audio source localization. A commercially available microphone array assembly (consisting of four microphones) has been interfaced to Raspberry Pi 3B for estimating the arrival angles of audio/sound/acoustic sources. The acoustic signals received a teach of the microphones are used for DOA estimation using the Generalized Cross Correlation Phase Transform (GCC-PHAT) algorithm. The estimated DOA is then displayed on the serial monitor. Servo motors are interfaced to the Raspberry Pi and are programmed to point towards the estimated direction of the audio source (on the basis of the estimated DOA value). The estimated DOA value is also updated to the cloud-based server Firebase using the inbuilt Wi-Fi on Raspberry Pi, and can be remotely accessed through a smartphone running on android platform. The proposed system has been designed and tested successfully and it is observed that it provides accurate DOA estimation and audio source tracking in real-time. This has various uses in applications like voice assistant robots, machine audition, hearing aids, source remixing, augmented reality headsets, security cameras, sound tracking (sonar sensor) devices, bird identification, mammal localization and so on.

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Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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“…The efficiency of the algorithms of the image blind separation is valued by their separability performance index (PI) which is determined with the FPGA processor. The index (PI) is given by the below equation [7], [8]:…”

Section: Evaluation Criteria For Image Separationmentioning

confidence: 99%

FPGA based Hardware Co-Simulation Implementation for RealTime Image Blind Separation using ICA Algorithms

Benahmed¹,

Mekhfioui²,

Guennoun³

2022

IJETAE

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The use of mixed image sources requires digital preprocessing to separate them, for making easier their storage and improve their quality. The ICALAB for Image Processing method is one of the solutions proposed for the blind efficient separation of signals without prior knowledge of the source images. Nevertheless, to choose the appropriate algorithm with the most efficient separation index, several tests are carried out on a certain number of algorithms, with off-line processing of image samples bases, by means of software tools which are generally installed on a computer, such as ICALAB, TIPICA, GIFT, NMFLAB, model identification UBSS, ... Thus, in order to perform the image separation processing in real time, this study aims to develop a rapid prototyping embedded system for implementing the blind source separation concept. It mainly provides a promising approach for implementing twelve efficient blind image separation algorithms on the ZYBO Z7 FPGA board, using Xilinx System Generator (XSG) for Real Time Hardware Co-simulation. As an application example, this study deals with the blind separation of two e-frames mixture, and then provides the adequate algorithm, basing on the calculation of the Performance Index (PI), and the separated images results. Keywords—Blind Image Separation; Hardware CoSimulation; Independent Component Algorithm (ICA); Xilinx System Generator (XSG); Zynq-7000; VHDL.

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A Comparative Approach of Blind Source Separation with Arduino Due and TMS320C6713

Cited by 2 publications

References 6 publications

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

FPGA based Hardware Co-Simulation Implementation for RealTime Image Blind Separation using ICA Algorithms

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