Accuracy comparisons of fingerprint based song recognition approaches using very high granularity.

Music and song recognition is an activity of wide interest for researchers and companies due to the intrinsic challenges and the possible economical profits it can give. Despite basic algorithms about song recognition are simple in principle, it is quite difficult to obtain an efficient and robust approach able to generate an effective algorithm for identifying short piece of audio on the fly. In this paper, we compare the results obtained using a new algorithm we recently proposed against several baseline approaches in terms of accuracy when very short pieces of audio are processed. Experimental results, performed using both a subset of the MTG-Jamendo dataset and a proprietary audio corpus containing 7000 songs, show our approach outperform the others in particular for excerpts of audio shorter than 3s.

A new approach for investigating intracranial pressure signal: filtering and morphological features extraction from continuous recording.

Nowadays, the Intracranial Pressure (ICP) monitoring has become the most common method of investigation for both traumatic and chronic neural pathologies. ICP signals are typically triphasic, that is, in a single waveform, three subpeaks can be identified. This work outlines a new algorithm to identify subpeaks from the ICP recordings and to extract a number of 20 meaningful parameter trends. The validity of the implemented method has been proved through a comparison between the automatic subpeaks identification by the algorithm and the manually marked subpeaks by a neurosurgeon. The automatic marking system has identified subpeaks for the 63.74% (mean value) of pulse waves, providing the position and amplitude of each identified subpeak within a tolerance of ±7 samples. This automatic system provides a feature set to be used by classification software to obtain more precise and easier diagnosis in all those cases that involve brain damages or diseases.