In the context of content analysis for indexing and retrieval, a method for creating automatic music mood annotation is presented. The method is based on results from psychological studies and framed into a supervised learning approach using musical features automatically extracted from the raw audio signal. We present here some of the most relevant audio features to solve this problem. A ground truth, used for training, is created using both social network information systems (wisdom of crowds) and individual experts (wisdom of the few). At the experimental level, we evaluate our approach on a database of 1000 songs. Tests of different classification methods, configurations and optimizations have been conducted, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness against different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed. This real world application demonstrates the usability of this tool to annotate large-scale databases. We also report on a user evaluation in the context of the PHAROS search engine, asking people about the utility, interest and innovation of this technology in real world use cases.

A robust and efficient technique for automatic music mood annotation is presented. A song's mood is expressed by a supervised machine learning approach based on musical features extracted from the raw audio signal. A ground truth, used for training, is created using both social network information systems and individual experts. Tests of 7 different classification configurations have been performed, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness to different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed.

A key aspect of architecture-centric development is the traceability of design documentation. In particular, architects should be always aware of the relationships between the architectural model "as documented" and its corresponding implementation model. The problem is that these two models usually diverge from each other over time, due to factors such as new requirements, refactorings, etc. Therefore, tool assistance is very important to assess the level of conformance between architectural documentation and implementation. In this paper, we present a tool approach called ArchSync that helps architects to conciliate architectural documentation expressed through Use-Case Maps with Java source code, as modifications are being made on the code. ArchSync relies on a heuristic that incrementally detects inconsistencies with respect to the architectural prescriptions, based on the analysis of system execution traces. ArchSync can also give suggestions for re-synchronization. Results of two case-studies showing the applicability of the approach are reported.