Packet loss concealment method based on hidden Markov model and decision tree for AMR-WB codec

Abstract

In this paper, we introduce hidden Markov model (HMM) based packet loss concealment (PLC) methods and discuss the impact of Markovian assumptions on the performance of these models. We also present a new PLC method, implemented on the G.722.2 codec, which relies on the HMM and Decision Tree (DT), namely HMDT architecture, to enhance the perceptual quality of Voice over Internet Protocol (VoIP) communications under severe packet loss conditions. The proposed method is a receiver-based model that tracks the statistical evolution of speech signals through HMM and uses the DT architecture to predict/estimate accurately the lost speech packets by exploiting the surrounding received speech packets. Objective and subjective metrics are used to evaluate the performance of the proposed method. Test results show that our proposed method enhances considerably the speech quality of the reconstructed speech signal and produces a more natural speech variation compared to conventional PLC methods.

Big data-driven risk decision-making and safety management in agricultural supply chains

“…The fundamental concept behind utilizing the decision tree algorithm with transition structures for examining risk factors in agricultural supply chains and their interconnections entails merging the decision tree model with the theory of Hidden Markov Model (HMM) (Gueham and Merazka, 2024;Nagahama et al, 2014). This results in a composite model that can effectively capture temporal dependencies and implicit states in sequential data.…”