Augvr by PhaseCvlt ๐๏ธ
Real-time audio analysis: freq, stereo, loudness, phase, waveform + 6 viz modes (spec, osc, wave, Lissajous, spectrogram, 3D)
๐ป Mac/Win (AU/VST3)
๐ FREE https://phasecvlt.com/products/augvr/
More freeware ๐ https://linktr.ee/legalvst
#freeplugin #phasecvlt #audioanalysis #spectrumanalyzer #oscilloscope #lufs #sidechain #visualizer
Shazam's song recognition is proprietary, with no transparency on how it works. No open source models or benchmarks to compare against. This is bad science and software engineering - hiding algorithms behind a paywall. Shazam should release models for peer review and open source inference code.
Scepter Lite by Mercurial Tones
Real-time FFT, Mid/Side mode, RMS/peak metering, resizable UI
๐ป Mac/Win (AU, VST3, Standalone)
๐ FREE https://www.mercurialtones.com/scepter-lite
More freeware ๐ https://linktr.ee/legalvst
#freeplugin #spectrumanalyzer #audioanalysis #vstplugin #musicproduction #audiotools
Spectra99 by Hakan Yurdakul ๐
Razor-sharp spectrum analyzer w/ ultra-low leakage
๐ป Win (Standalone, VST3)
๐ FREE https://hakanyurdakul.com/spectra99/
More freeware ๐ https://linktr.ee/legalvst
#freeplugin #spectrumanalyzer #vst #audioanalysis #musicproduction #audiotools
Journal of Open Source Software: voice: A Comprehensive R Package for Audio Analysis
{voice}
"...a free, open-source toolkit designed to streamline audio analysis by integrating music theory and advanced computational techniques. It enables researchers to extract, summarize, and analyze voice data efficiently, supporting applications such as speech recognition, speaker identification, and mood inference..."
Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.
Anybody know of a straightforward way to analyse the sound recordings from SAQ transmissions and plot how the frequency varies over time? ๐
AlgoCompSynth-One v2.1.0 has been released
https://github.com/AlgoCompSynth/AlgoCompSynth-One/releases/tag/v2.1.0
#AlgorithmicComposition
#AudioAnalysis
#AudioSynthesis
#GPUAudio
#PyTorch
#DifferentiableDigitalSignalProcessing
Using audio for open-source intelligence analysis Introduction Open-source intelligence analysis often heavily relies on video analysis. There will be use cases for researchers to look at video footage to find people, buildings or other things that will be a form of verification and or validation based on analyzing video footage using OSINT techniques. In most cases these techniques rely on image analysis techniques. OSINT researches study video footage and look for details in: The foreground T
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Data Quine
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Zorro Notorious MEB ๐ก
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