Development and use of a new Speech Quality Evaluation Parameter ESNR using ANN and Grey Wolf Optimizer

197-200The performance of Speech Enhancement (SE) Algorithms is evaluated using various objective and subjective evaluation parameters. Recently, few objective evaluation parameters are developed for the measurement of speech quality and intelligibility. But still, there are ample scopes determining statistical parameters to predict the SNR of a noisy speech signal without using any reference of clean signal and noise. In this paper, this problem has been addressed and three types of Artificial Neural Networks (ANN) are developed for efficient prediction of the estimated SNR (E-SNR) of a given noisy speech signal. To further improve the accuracy of prediction of the SNR of the ANN, the coefficients of ANN are tuned using the bio-inspired optimization technique. In this paper, a popular and efficient Grey wolf Optimization is chosen for the purpose. Several audio features are studied and appropriate features are chosen as the inputs to the ANN. Finally, a comparative performance analysis is carried out using two standard speech databases and the best performing ANN and audio features are identified to provide the best ESNR

Development and use of a new Speech Quality Evaluation Parameter ESNR using ANN and Grey Wolf Optimizer

The performance of Speech Enhancement (SE) Algorithms is evaluated using various objective and subjective evaluation parameters. Recently, few objective evaluation parameters are developed for the measurement of speech quality and intelligibility. But still, there are ample scopes determining statistical parameters to predict the SNR of a noisy speech signal without using any reference of clean signal and noise. In this paper, this problem has been addressed and three types of Artificial Neural Networks (ANN) are developed for efficient prediction of the estimated SNR (E-SNR) of a given noisy speech signal. To further improve the accuracy of prediction of the SNR of the ANN, the coefficients of ANN are tuned using the bio-inspired optimization technique. In this paper, a popular and efficient Grey wolf Optimization is chosen for the purpose. Several audio features are studied and appropriate features are chosen as the inputs to the ANN. Finally, a comparative performance analysis is carried out using two standard speech databases and the best performing ANN and audio features are identified to provide the best ESNR

Integrated swarm intelligence and IoT for early and accurate remote voice-based pathology detection and water sound quality estimation

In smart city design, artificial intelligence, health optimization, and the Internet of medical things are crucial in developing machine learning-based medical data analytics. The two main components of this approach are the Internet of Things (IoT) and swarm intelligence integration on the use of the Internet of Medical Things. The analysis of human speech and audio signals plays a crucial role which indicates physical and psychological well-being. Selecting appropriate features is very important and can greatly impact the overall signal processing and computation when developing these models based on speech applications. In the audio signal processing field, many features are available in the time, frequency, and statistical domain, and selecting proper features is a tedious task. But after feature selection, the stability analysis of these techniques is also equally important. This study considers these two problems by applying swarm intelligence-based feature selection techniques with higher stability. The optimized selected features are used for remote detection of voice-based pathological diseases, environmental sound detection in smart cities, acoustic sound quality assessment in amusement parks, and its impact on human psychological health. The proposed feature selection techniques are observed to perform comprehensively better than the standard speech features-based models in terms of both performance and computational complexities