Posts by Michael Pitzrick
Spectrogram Theory
Introduction Spectrograms are produced by a procedure known as the short-time Fouriertransform (STFT). The articles and videos below will give you a deeper understanding of how Raven converts your digital audio recordings into plots in the spectrogram, selection spectrum, and spectrogram slice views, as well as the theoretical limits to time and frequency resolution. Discussion…
Read MoreError Opening Selection Table
DESCRIPTION Raven users sometimes report that they cannot open their selection Tables in Raven. The solution depends on the observed symptom. SOLUTIONS Raven displays an error dialog stating “insert wording of error dialog”. (insert screenshot of error message) You accidentally selected “Open Sound Selection Table” from the “File” menu. Select “Open Selection Table”. The solution…
Read MoreTroubleshooting Raven Pro and Raven Lite on Linux Systems
Introduction Raven Pro and Raven Lite User’s have reported several problems when attempting to install the software on Linux systems. I have listed solutions to these problems below in the order you are likely to encounter them during installation. Note that we have tested Raven Pro and Raven Lite only in Ubuntu. Solutions
Read MoreAutomated Measurements
Introduction Documentation for automated measurements in Raven Pro can be found in several places. This article is intended as a summary. Note that Raven Lite does not allow the user to add automated measurements. Discussion Automated measurement fields can be added to a selection table in Raven Pro two ways: You can see a demonstration…
Read MoreAutomated and Robust Measurement of Signal Features
Dr. Kathryn A. Cortopassi January 2006 INTRODUCTION Measurement of signal characteristics (or features) is a crucial step in many bioacoustic studies. Such feature sets can, for example, form the basis of analyses relating signal attributes to various biological measures and contexts, or of recognition-discrimination tasks providing for automatic extraction of relevant signals from large data…
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