Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM

The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integra...

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Main Authors: Ignacio Algredo-Badillo, Brenda Sánchez-Juárez, Kelsey A. Ramírez-Gutiérrez, Claudia Feregrino-Uribe, Francisco López-Huerta, Johan J. Estrada-López
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Technologies
Subjects:
Online Access:https://www.mdpi.com/2227-7080/10/4/86
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author Ignacio Algredo-Badillo
Brenda Sánchez-Juárez
Kelsey A. Ramírez-Gutiérrez
Claudia Feregrino-Uribe
Francisco López-Huerta
Johan J. Estrada-López
author_facet Ignacio Algredo-Badillo
Brenda Sánchez-Juárez
Kelsey A. Ramírez-Gutiérrez
Claudia Feregrino-Uribe
Francisco López-Huerta
Johan J. Estrada-López
author_sort Ignacio Algredo-Badillo
collection DOAJ
description The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%.
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spelling doaj.art-7a25998c405649ccab77cf23f41b7d022023-11-30T22:34:58ZengMDPI AGTechnologies2227-70802022-07-011048610.3390/technologies10040086Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSMIgnacio Algredo-Badillo0Brenda Sánchez-Juárez1Kelsey A. Ramírez-Gutiérrez2Claudia Feregrino-Uribe3Francisco López-Huerta4Johan J. Estrada-López5Consejo Nacional de Ciencia y Tecnología-Instituto Nacional de Astrofísica, Óptica y Electrónica (CONACYT-INAOE), Luis Enrique Erro 1, Santa María Tonanzintla, Puebla 72840, MexicoUniversidad Politécnica de Tlaxcala, Av. Universidad Politécnica No.1, San Pedro Xalcaltzinco, Zacatelco 90180, MexicoConsejo Nacional de Ciencia y Tecnología-Instituto Nacional de Astrofísica, Óptica y Electrónica (CONACYT-INAOE), Luis Enrique Erro 1, Santa María Tonanzintla, Puebla 72840, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa María Tonanzintla, Puebla 72840, MexicoFacultad de Ingeniería Eléctrica y Electrónica, Universidad Veracruzana, Boca del Río 94294, MexicoPhysics and Engineering Department, Westmont College, Santa Barbara, CA 93108, USAThe significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%.https://www.mdpi.com/2227-7080/10/4/86FPGAaudio fingerprintinghardware architectureSSM
spellingShingle Ignacio Algredo-Badillo
Brenda Sánchez-Juárez
Kelsey A. Ramírez-Gutiérrez
Claudia Feregrino-Uribe
Francisco López-Huerta
Johan J. Estrada-López
Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
Technologies
FPGA
audio fingerprinting
hardware architecture
SSM
title Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
title_full Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
title_fullStr Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
title_full_unstemmed Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
title_short Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM
title_sort analysis and hardware architecture on fpga of a robust audio fingerprinting method using ssm
topic FPGA
audio fingerprinting
hardware architecture
SSM
url https://www.mdpi.com/2227-7080/10/4/86
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