Digital audio authentication by robust feature embedding

An Automatic Digital Audio Authentication/Forensics System

IEEE Access ◽

10.1109/access.2017.2672681 ◽

2017 ◽

Vol 5 ◽

pp. 2994-3007 ◽

Cited By ~ 5

Author(s):

Zulfiqar Ali ◽

Muhammad Imran ◽

Mansour Alsulaiman

Keyword(s):

Digital Audio ◽

Audio Authentication

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Machine Learning in Authentication of Digital Audio Recordings

Machine Learning and Deep Learning in Real-Time Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-3095-5.ch007 ◽

2020 ◽

pp. 145-167

Author(s):

Rashmika Kiran Patole ◽

Priti Paresh Rege

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Digital Audio ◽

Acoustic Environment ◽

Feature Sets ◽

Audio Forensics ◽

Audio Recordings ◽

Audio Authentication ◽

Environment Identification

The field of audio forensics has seen a huge advancement in recent years with an increasing number of techniques used for the analysis of the audio recordings submitted as evidence in legal investigations. Audio forensics involves authentication of the evidentiary audio recordings, which is an important procedure to verify the integrity of audio recordings. This chapter focuses two audio authentication procedures, namely acoustic environment identification and tampering detection. The authors provide a framework for the above-mentioned procedures discussing in detail the methodology and feature sets used in the two tasks. The main objective of this chapter is to introduce the readers to different machine learning algorithms that can be used for environment identification and forgery detection. The authors also provide some promising results that prove the utility of machine learning algorithms in this interesting field.

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Application of Power System Frequency for Digital Audio Authentication

IEEE Transactions on Power Delivery ◽

10.1109/tpwrd.2012.2198892 ◽

2012 ◽

Vol 27 (4) ◽

pp. 1820-1828 ◽

Cited By ~ 23

Author(s):

Yuming Liu ◽

Zhiyong Yuan ◽

Penn N. Markham ◽

Richard W. Conners ◽

Yilu Liu

Keyword(s):

Power System ◽

Digital Audio ◽

Audio Authentication ◽

System Frequency

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Clinical Success Using the Audio-Visual Feedback Training for Cluttering

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_pers-sig17-2019-0018 ◽

2019 ◽

Vol 4 (6) ◽

pp. 1589-1594

Author(s):

Yvonne van Zaalen ◽

Isabella Reichel

Keyword(s):

Visual Feedback ◽

Clinical Success ◽

Pause Duration ◽

Digital Audio ◽

Male Adolescents ◽

Feedback Training ◽

Positive Effects ◽

Speech Monitoring ◽

Adolescents And Adults

Purpose Among the best strategies to address inadequate speech monitoring skills and other parameters of communication in people with cluttering (PWC) is the relatively new but very promising auditory–visual feedback (AVF) training ( van Zaalen & Reichel, 2015 ). This study examines the effects of AVF training on articulatory accuracy, pause duration, frequency, and type of disfluencies of PWC, as well as on the emotional and cognitive aspects that may be present in clients with this communication disorder ( Reichel, 2010 ; van Zaalen & Reichel, 2015 ). Methods In this study, 12 male adolescents and adults—6 with phonological and 6 with syntactic cluttering—were provided with weekly AVF training for 12 weeks, with a 3-month follow-up. Data was gathered on baseline (T0), Week 6 (T1), Week 12 (T2), and after follow-up (T3). Spontaneous speech was recorded and analyzed by using digital audio-recording and speech analysis software known as Praat ( Boersma & Weenink, 2017 ). Results The results of this study indicated that PWC demonstrated significant improvements in articulatory rate measurements and in pause duration following the AVF training. In addition, the PWC in the study reported positive effects on their ability to retell a story and to speak in more complete sentences. PWC felt better about formulating their ideas and were more satisfied with their interactions with people around them. Conclusions The AVF training was found to be an effective approach for improving monitoring skills of PWC with both quantitative and qualitative benefits in the behavioral, cognitive, emotional, and social domains of communication.

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