sebastianmenze/ADCP_bottom_detection

Stars
3
Rank 3,963,521 (Top 79 %)
Language
MATLAB
Created over 8 years ago
Updated almost 7 years ago

sebastianmenze/ADCP_bottom_detection

sebastianmenze

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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

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.

Tindeq-Progressor-climbing-strength-test-server

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

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.