This paper proposes a data hiding approach to embed data on compressed speech bit stream in order to transmit two simultaneous speeches instead of one. The host and embedded signals are Enhanced Full Rate (EFR) and Mixed-Excitation Linear Predictive enhanced (MELPe) encoded speech bit streams. Host and hidden speech quality is determined by Perceptual Evaluation of Speech Quality (PESQ) which is an objective testing. The effect of embedding data to each specific bit of EFR coefficients, on speech quality has been investigated and the less important bits are selected to embed data. Meanwhile, to achieve a higher speech quality, proposed method is modified and an adaptive algorithm is developed. We present a full procedure and the results of the performance tests.
Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex software projects. As a result, several automatic comment generation models are developed as a solution. The recent models explore the integration of external knowledge resources such as Unified Modeling Language class diagrams to improve the generated comments. In this paper, we propose API2Com, a model that leverages the Application Programming Interface Documentations (API Docs) as a knowledge resource for comment generation. The API Docs include the description of the methods in more details and therefore, can provide better context in the generated comments. The API Docs are used along with the code snippets and Abstract Syntax Trees in our model.We apply the model on a large Java dataset of over 130,000 methods and evaluate it using both Transformer and RNNbase architectures. Interestingly, when API Docs are used, the performance increase is negligible. We therefore run different experiments to reason about the results. For methods that only contain one API, adding API Docs improves the results by 4% BLEU score on average (BLEU score is an automatic evaluation metric used in machine translation). However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input. Our results confirm that the API Docs can be useful in generating better comments, but, new techniques are required to identify the most informative ones in a method rather than using all documentations simultaneously.
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