Mobile Audio Intelligence
Lazaros Vrysis, Nikolaos Tsipas, Charalampos A. Dimoulas, George V. Papanikolaou · 2015
The task of general audio detection and segmentation based in means of machine learning is very popular and high-demanding procedure nowadays. Most relevant works in the last decade aim at modelling audio in order to conduct a semantics analysis and a high--level categorization. A generic strategy that would detect audio events as means of transitions from one audio state to another is considered interesting and would support whole classification workflow. This work investigates the possibilities in designing a robust bimodal segmentation algorithm for audio that would perform well in different conditions without relying on complicated machine learning schemes by minimizing prior knowledge for detection model, and thus, delivering consistent performance for any input signal and computing environment. Additionally, a modern user-generated content approach for populating and updating ground truth databases is presented. Both techniques are implemented and embedded as upgrades, in a mobile software environment for smartphones.