dejavu
pyAudioAnalysis
dejavu | pyAudioAnalysis | |
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15 | 11 | |
6,322 | 5,673 | |
- | - | |
0.0 | 5.0 | |
15 days ago | about 1 month ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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dejavu
- Audio Fingerprinting and Recognition in Python
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Contacting Collectors or Creating API to help with searching
This doesn't seem hard, you can use something like this to dwoanload the songs: https://stackoverflow.com/a/27481870/6151784 and something like this to calculate how much they match: https://github.com/worldveil/dejavu The question is would you create a (dedicated) server to do your work? Or your own pc? You could also create a very simple page where someone would paste you a YouTube profile URL and you would check all songs of this URL. Also to have a db and save information about the matching and which youtube profiles have alsready been checked. Something like that could work.
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Tiny bit of experience but need to compile a Github program. What is the best video / resource to learn to do this quickly?
If you read the installation.md file it clearly states that it has only been tested on UNIX systems, so you might be on your own trying to get it to wor in windows.
- Help needed with school project
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Identification of all usages of OSTs in Made in Abyss (S1)
Using neural networks seems complicated, did you tried audio fingerprinting? I have been using this audio fingerprinting library to power this anime song synchronization script. You can check Panako and dejavu too.
- Dejavu – Audio fingerprinting and recognition algorithm
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fingerprinting sections of audio from file
I want to say these few seconds match these few seconds from a different audio track. Using dejavu raw has overhead I do not need/want and hence I've been fiddling around with the fingerprint script. When modifying the global variables I can get better hits or worse hits, I will admit that even after reading there recommended article and many other sources, I can't find some good explanations about the mathematics behind the filtering after the specgram has been applied. As far as a I am aware we first apply filters to find/make fine points across the spectrogram after that we only check the distance between points along the time axis not the frequency or a hypotenuse (weird).
- Some information and advice about DDoS, from someone who was there during #opPayback
- List of resources
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Uploading an audio dataset into a database for comparison
I used a repo called https://github.com/worldveil/dejavu to compare audio hashed fingerprints and distinguish the difference between them.
pyAudioAnalysis
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How would I compare two voice recordings of the same sentence and advise one speaker how to get closer to the second?
I actually came up with an el cheapo version of what I want to accomplish that isn't perfect but without any research can implement it and it may actually prove useful to language learners. PM me if you're interested in hearing it and critiquing it. I can share here that I'm using this guy's multiple repos though: https://github.com/tyiannak/pyAudioAnalysis
- How do I run code only when an audio file has bass
- A Python library for audio feature extraction, classification, segmentation and applications
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Phonetic search for audio files
Update: From one researcher to another. I was referred to a Python Audio AI project . Once I determine exactly which module to use I should be smooth sailing. I'll send more updates soon.
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Clustering songs with different lengths
Hey folks, I'm looking into clustering audio files with features extracted by pyAudioAnalysis. However, every feature (I'm interested in MFCC, spectral centroid and spread, and BPM) is extracted for each frame of the song (by default 0.05s, excluding BPM that relates to the whole) so tracks with different lengths produce arrays with different shapes.
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AUDIO ANALYSIS WITH LIBROSA
To learn more about pyAudioAnalysis here you go.
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Creating Audio Features with PyAudio Analysis
Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy? This is where PyAudio Analysis comes in. PyAudio Analysis is an open source Python project by Theodoros Giannakopoulos, a Principle researcher of multimodal machine learning at the Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL). The package aims to simplify the feature extraction and classification process by providing a number of helpful tools at can sift through the signal and create relevant features. These features can then be used to train models for classification tasks.
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[P] Feature extraction for acoustic signals
This might be relevant, which has a set of feature extraction methods implemented: https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction
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Hacker News top posts: Dec 11, 2021
A library for audio feature extraction, regression, classification, segmentation\ (2 comments)
- Audio feature extraction, classification, segmentation and applications
What are some alternatives?
django-elastic-transcoder - Django + AWS Elastic Transcoder
librosa - Python library for audio and music analysis
m3u8 - Python m3u8 Parser for HTTP Live Streaming (HLS) Transmissions
pydub - Manipulate audio with a simple and easy high level interface
audiolazy - Expressive Digital Signal Processing (DSP) package for Python
SpeechRecognition - Speech recognition module for Python, supporting several engines and APIs, online and offline.
speech-to-text-websockets-python
pyAcoustics - A collection of python scripts for extracting and analyzing acoustics from audio files.
pyechonest - Python client for the Echo Nest API
mingus - Mingus is a music package for Python
id3reader - Id3reader.py is a Python module that reads ID3 metadata tags in MP3 files.
Watson Developer Cloud Python SDK - :snake: Client library to use the IBM Watson services in Python and available in pip as watson-developer-cloud