A library for soundscape synthesis and augmentation
Soundscape analysis with BirdNET.
A list of awesome open-source acoustic packages and resources.
This is an audio application that produces 3D binaural audio from 2D mono audio samples and positional information given by the graphical user interface. Listen to 3D audio through stereo headphones. Video Demo:https://www.youtube.com/watch?v=peF9cZSwVGw
Phanary is a lightning-fast, free, online app for desktop and mobile that excels at finding and playing atmospheric music and sound effects for games like D&D and Pathfinder. Phanary is meant to be used in a Game Master's preparation and improvisation, and to be quick and streamlined enough to smoothly handle the party going somewhere unexpected.
Tools of soundscape information retrieval, this repository is a developing project. Please go to https://github.com/meil-brcas-org/soundscape_IR for full releases.
An iOS application/service that aids navigation through spatialized audio
A Java audio-playback class, modeled on javax.sound.sampled.Clip, enhanced with concurrent playback and dynamic handling of volume, pan and frequency. Maven version.
An open toolbox of soundscape information retrieval
Desktop soundscape application
Unsupervised classification to improve the quality of a bird song recording dataset. https://doi.org/10.1016/j.ecoinf.2022.101952
Web application for ecoacoustics to manage, navigate, visualise, annotate, and analyse soundscape recordings.
A more powerful, intuitive, and concurrency safe Clip, for Java audio needs.
Workflow Integration-Tools for the user community of d&b Soundscape.
Ashdeck is open source productivity app for blocking distractions and boasting focus.
Process ocean audio data archives to daily analysis products of hybrid millidecade spectra using PyPAM.
This is the source for an Android version of the original iOS Soundscape app
A Flask app to generate soundscapes from pictures and videos
Website that streams or casts relaxing soundscapes.
Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.
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