This paper introduces a new algorithm for phase reconstruction from a modified magnitude spectrogram. Conventional methods are based on specific optimization process and classical overlap-add procedure. By referring to previous works, it is observed that use of local patterns may also provide good reconstructions. This leads to a reconstruction technique based on iterative inference of local patterns. The resulting method is suitable for incremental and real-time processing. It may be used directly, or as initial-guess to an optimization algorithm. The paper explores variants of models based local patterns. The results of these variants are compared.

PyCVF is an open source framework for computer vision and video-mining. It allows rapid development of applications and it provides standardized tools for common operations such as : browsing datasets, applying transformations to one dataset on-the-fly, computing features, indexing multimedia datasets, querying for nearest-neighbors, training a statistical model, or browsing the result in a 3d-space. PyCVF has a Python API, it also provides command line programs, QT Gui, and a web front-end. It can interacts nicely with other leading frameworks such as Weka, Orange, OpenCV, Django...