Robust Automatic Speaker Identification System Using Shuffled MFCC Features

Barhoush, Mahdi; Hallawa, Ahmed; Schmeink, Anke

doi:10.1109/icmlant53170.2021.9690530

Cited by 5 publications

(1 citation statement)

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“…Subsequently, authors in [251] proposed a DL-based speaker identification mechanism using improved shuffled MFCC (SHMFCC). The data augmentation approach is used in conjunction with the extraction of shuffled MFCC features.…”

Section: ) Speaker Identification (Sid) System For Forensicsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Section: ) Speaker Identification (Sid) System For Forensicsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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Speaker identification and localization using shuffled MFCC features and deep learning

Barhoush

Hallawa

Schmeink

2023

Int J Speech Technol

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The use of machine learning in automatic speaker identification and localization systems has recently seen significant advances. However, this progress comes at the cost of using complex models, computations, and increasing the number of microphone arrays and training data. Therefore, in this work, we propose a new end-to-end identification and localization model based on a simple fully connected deep neural network (FC-DNN) and just two input microphones. This model can jointly or separately localize and identify an active speaker with high accuracy in single and multi-speaker scenarios by exploiting a new data augmentation approach. In this regard, we propose using a novel Mel Frequency Cepstral Coefficients (MFCC) based feature called Shuffled MFCC (SHMFCC) and its variant Difference Shuffled MFCC (DSHMFCC). In order to test our approach, we analyzed the performance of the identification and localization proposed model on the new features at different noise and reverberation conditions for single and multi-speaker scenarios. The results show that our approach achieves high accuracy in these scenarios, outperforms the baseline and conventional methods, and achieves robustness even with small-sized training data.

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