AeonWave is a cross platform, hardware accelerated, multi-threaded, and versatile stereo and 4D audio mixing library. By combining hardware accelerated rendering and a low memory footprint the library can handle almost any situation, from virtual synthesizer apps for smart phones to immersive simulation. It has a small, easy to use, fault tolerant, and consistent API, low latency support by using hardware acceleration, simultaneous support for stereo and 4D audio, audio frames with sub-mixing capabilities, support for stereo and 3D audio effects and filters, and a Freeware supplemental OpenAL implementation.
The JAVE (Java Audio Video Encoder) library is a Java wrapper on the ffmpeg project. Developers can take take advantage of JAVE to transcode audio and video files from one format to another. For example, you can transcode an AVI file to an MPEG one, you can separate and transcode audio and video tracks, and you can resize videos, changing their sizes and proportions. Many other formats, containers, and operations are supported by JAVE.
DromeAudio is a small audio manipulation and playback library. It features a simple API for loading, generating, processing, and playing audio. Some of its features include loading and saving WAV sounds, loading Ogg Vorbis sounds, audio mixing/playback, and dynamic audio processing effects such as pitch shifting and echo.
The Python audio processing suite is a module that contains a variety of convenience functions to process audio signals. It can be used to plot spectral analyses of a song across time and to quickly ascertain encoding quality, but the instrumental goal of this suite is to robustly identify duplicated songs, regardless of which album they were released in, encoding quality, or start time.
pHash is an implementation of various perceptual hashing algorithms. A perceptual hash is a fingerprint of a multimedia file derived from various features from its content. Unlike cryptographic hash functions that rely on the avalanche effect of small changes in input leading to drastic changes in the output, perceptual hashes are "close" to one another if the features are similar. Potential applications include copyright protection, similarity searches for media files, or even digital forensics.