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With the purpose of further mastering and grasping the course of speech signal processing, a novel Android-based, mobile-assisted educational platform (AEPS) is proposed in this paper. The goal of this work was to design AEPS as an educational signalprocessing auxiliary system by simulating signal analysis methods commonly used in speech signal processing and bridging the gap for transition from undergraduate study to industry practice or academic research. The educational platform is presented in a highly intuitive, easy-to-interpret and strongly maneuverable graphical user interface. It also has the characteristics of high portability, strong affordability, and easy adoptability for application extension and popularization. Through adequate intuitive user interface, rich visual information, and extensive hands-on experiences, it greatly facilitates students in authentic, interactive, and creative learning. This paper details a subjective evaluation of AEPS's effectiveness as an educational tool. The result of the experiences shows that the proposed platform not only promotes the students' learning interest and practical ability but also consolidates their understanding and impression of theoretical concepts.

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Purpose Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices. Design/methodology/approach This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics. Findings The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors’ previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%. Research limitations/implications With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio. Originality/value This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.

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