librosa
meyda
librosa | meyda | |
---|---|---|
14 | 7 | |
6,699 | 1,391 | |
1.1% | 0.1% | |
7.2 | 5.2 | |
22 days ago | 6 days ago | |
Python | TypeScript | |
ISC License | MIT License |
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librosa
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Open Source Libraries
librosa/librosa: Python library for audio and music analysis
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A Cross-Platform library for audio spectrogram and feature extraction, support mobile real-time computing
How does this compare to mature libraries for other platforms like librosa?
- Precious Advices About AI-supported Audio Classification Model
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What are the common audio feature tool libraries in python?
I use librosa now. What other useful audio feature extraction libraries are there?
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Looking for a program that will examine a folder full of mp3s or flacs and list out ones with lower or higher than average volume
librosa can do that easily but I think there is an easier way to find what are you looking for:
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Get amplitude of every audio frame of .wav
I have a .wav file, and using python, I'd like to get a list of every audio frame where the amplitude is at the resting position. How could I achieve this? I think the librosa library could do such a thing, but I'm struggling to find exactly how to do it. Any help would be greatly appreciated, thank you.
- Show HN: I'm building a browser-based DAW
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AUDIO ANALYSIS WITH LIBROSA
Librosa is a Python package developed for music and audio analysis. It is specific on capturing the audio information to be transformed into a data block. However, the documentation and example are good to understand how to work with audio data science projects.
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AUDIO CLASSIFICATION USING DEEP LEARNING
Hello! welcome once again to the continuation of the last blog post about audio analysis using the Librosa python library, if you missed this article don't worry here you can enjoy audio analysis techniques with Librosa.
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DATA AUGMENTATION IN NATURAL LANGUAGE PROCESSING
Changing pitch of the audio:- in this technique python package for audio analysis like Librosa is the best tool to go with, by adding effect on the audio pitch to create new audio data.
meyda
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Show HN: I'm building a browser-based DAW
Sounds like a job for web workers. Do you have any examples? I'd love to check them out. I've seen some sequencers but never a full-blown DAW attempt. I've been toying with a couple browser-based, realtime audio ML ideas lately (mostly porting some models to Tensorflow.js), so my interest is piqued.
As far as libraries go for analysis, the only solid option I've found so far is Meyda[0]. I was drawn to it mostly because it closely maps to librosa[1], and it seems fairly mature. Does anyone have any others that may come in handy for this kind of work? This is just free-time tinkering for me. I'm completely new to the space.
[0]: https://meyda.js.org/
- GitHub - meyda/meyda: Audio feature extraction for JavaScript.
- Meyda: A JavaScript audio feature extraction library
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Normally my videos are educational/informational - but sometimes I get carried away in the studio.
It was pretty hacky really. There's a javascript library called Meyda that does audio processing and their front page has this visualization, i was just careful in my cropping. :). I should really give them credit in the description.
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What to do if you publish a beta build as @latest
I recently published a beta build of Meyda to the npm registry, with the intention of having one of our longest running users test it out to make sure it worked in their project. I hadn't done a manual release in a long time, since we use semantic-release, so I skimmed the output of npm publish --help, and figured out what command I would run. I set the version field of package.json to 5.1.7-beta.0, as instructed built the bundle, ran our test suite, and ran npm publish . --dry-run, to verify that the manifest of files that would be published was correct. It was correct, and so I ran
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Loading Audio in Node JS
If you're using Meyda to analyze audio that you load in this way, you will need to make sure that the sample rate of the audio matches the sample rate that Meyda is set to use. Otherwise you'll end up with audio features that are incorrect, and based on a skewed frequency scale. You can either match the Meyda sample rate to the wav sample rate, or you can resample the audio to fit a standard sample rate (i.e. 44,100hz, or 48,000hz). Resampling audio is a complicated topic beyond the scope of this article, but if you have trouble finding information online, let me know and I may find time to write an article.
- A curated list of Music DSP and audio programming resources
What are some alternatives?
pyAudioAnalysis - Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
howler.js - Javascript audio library for the modern web.
pydub - Manipulate audio with a simple and easy high level interface
camilladsp - A flexible cross-platform IIR and FIR engine for crossovers, room correction etc.
essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings
mistql - A query / expression language for performing computations on JSON-like structures. Tuned for clientside ML feature extraction.
kapre - kapre: Keras Audio Preprocessors
Cardinal - Virtual modular synthesizer plugin
beets - music library manager and MusicBrainz tagger
awesome-musicdsp - A curated list of my favourite music DSP and audio programming resources
audioread - cross-library (GStreamer + Core Audio + MAD + FFmpeg) audio decoding for Python
elk-pi - Elk Audio OS binary images for Raspberry Pi