Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios

A survey on artificial intelligence-based acoustic source identification

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

Multimedia Context Awareness for Smart Mobile Environments

openNowadays the development of the IoT framework and the resulting huge number of smart connected devices opens the door to exploit the presence of multiple smart nodes to accomplish a variety of tasks. Multimedia context awareness, together with the concept of ambient intelligence, is tightly related to the IoT framework, and it can be applied to a large number of smart scenarios. In this thesis, the aim is to study and analyze the role of context awareness in different applications related to smart mobile environments, such as future smart spaces and connected cities. Indeed, this research work focuses on different aspects of ambient intelligence, such as audio-awareness and wireless-awareness. In particular, this thesis tackles two main research topics: the first one, related to the framework of audio-awareness, concerns a multiple observations approach for smart speaker recognition in mobile environments; the second one, tied to the concept of wireless-awareness, regards Unmanned Aerial Vehicle (UAV) detection based on WiFi statistical fingerprint analysis.openXXXI CICLO - SC. E TECN. ING. ELETTR. E DELLE TEL. - Ambienti cognitivi interattiviGaribotto, Chiar