sebastianmenze/Tindeq-Progressor-climbing-strength-test-server

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sebastianmenze/Tindeq-Progressor-climbing-strength-test-server

sebastianmenze

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A python and bokeh application that creates a user interface to run climbing strength tests, that can store, visualize and send results

Processing-and-analysis-of-large-ADCP-datasets

This document will guide you through the steps necessary to process and analyse both vessel mounted (VM-ADCP) and lowered acoustic doppler current profilers (L-ADCP). Both are complex data sources that contain a lot of noise and potential biases, but dont despair, with todays programms and code packages anyone can work with this data and produce meaningfull results.

Python-Audio-Spectrogram-Explorer

A program to visualize audio files as spectrograms and log annotations. Developed to analyse marine mammal recordings, but can be used for many things.

ADCP_bottom_detection

An algorithm that detects the bottom in ADCP backscatter data as part of a Matlab package for ADCP processing

python-acoustic-backscatter-analysis

a collection of python scripts to analyse acoustic backscatter data from moored or vessel mounted echosounders

krillscan

Python based software to automatically analyse and transfer echosounder data

Vector-interpolation

How to interpolate vector data / current fields from sparse observations

Marine-mammal-call-detection-using-spectrogram-shape-matching

A detection algorithm to find and classify audio signals using a shape template

Processing-ADCP-data-from-IMR-vessels

A tutorial that shows how to find and post-process vessel mounted ADCP data from IMR vessels using the CODAS environment.

Analyzing-ambient-sound-recordings-from-Aural-M2-recorders-with-python

Analyzing ambient sound recordings from Aural M2 recorders with python

Transmission-loss-modeling-with-Bellhop-and-Matlab

A collection of matlab scripts to

acoustic_recorder_detection_range_modeling

Modeling the detection range of underwater recorders - using python, acoustic propagation models and ocean reanalysis data

Spectrogram-correlation-tutorial

This method uses 2-D template matching to detect a sound signal. The first step is to convert the sound signal into a spectrogram (2-d image) with suitable resolution. Than we need to define a template (also called a kernel) and slide it over the spectrogram to calculate the correlation score (between 0 and 1). All peaks above a certain threshold are than marked as detections.