Panako
pyacoustid
| Panako | pyacoustid | |
|---|---|---|
| 2 | 2 | |
| 175 | 313 | |
| - | 0.6% | |
| 4.0 | 6.1 | |
| 5 months ago | 6 months ago | |
| Java | Python | |
| GNU Affero General Public License v3.0 | MIT License |
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Panako
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Show HN: Pyzam, Shazam for DJs and Mixtapes in Python
Hello, really glad to see project like this popping up. I have few questions as I was working on something similar few years ago:
1. I did some development myself for a "Track Discovery for Djs"[1] project in this space of "dj music recognition" and I am wondering how are you able to handle mixtapes and dj mixes when there is a significant element of sound manipulation/distortion applied, like pitch/tempo + various effects? In my tests this totally confused the algorithms which were not designed to handle such cases.
2. Can you share which algorithm have you implemented for this project? I did read most of the research papers in this space and my preferred solution was to build upon https://github.com/JorenSix/Panako which I did.
In the space of "minimal microhouse techno" type of genre where there are often similar rhythm patterns or even tracks build up using same sample packs it proved to be more difficult to have reliable results than not.
I was investigating how Spotify and other market leaders can do track recognition and they do train ML models on the same track which has applied 100+ various different effects...
Curious to hear your thoughts...
[1] - https://rominimal.club
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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.
pyacoustid
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TMS' "fingerprint" question
I'm currently doing the same. I'm using the chromaprint library via the pyacoustid library, using the technique described here, which should give a certain amount of leeway in terms of pitch and other variables. See also the notes in the chromaprint documentation about truncating the samples to reduce noise.
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AcoustID Fingerprints Clarifications
That said, here are some things I've found useful while writing it. Maybe it inspires you to write your own. If you choose to do that I could answer some questions about specifics. https://github.com/beetbox/pyacoustid https://github.com/supermihi/pytaglib https://zenu.wordpress.com/2011/05/28/audio-fingerprinting-and-matching-using-acoustid-chromaprint-on-windows-with-python/ https://matpalm.com/resemblance/simhash/ https://beets.readthedocs.io/en/v1.5.0/plugins/duplicates.html
What are some alternatives?
stream-audio-fingerprint - Audio landmark fingerprinting in JavaScript
SpeechRecognition - Speech recognition module for Python, supporting several engines and APIs, online and offline.
Modulo7 - A semantic and technical analysis of musical scores based on Information Retrieval Principles
pytaglib - Python audio tagging library
XR3Player - 🎧 🎼 The MOST ADVANCED JavaFX Media Player
beets - music library manager and MusicBrainz tagger
audio-visualizer-android - 🎵 [Android Library] A light-weight and easy-to-use Audio Visualizer for Android.
youtube-dl - Unofficial daily builds for youtube-dl. DO NOT OPEN PULL REQUESTS HERE
dejavu - Audio fingerprinting and recognition in Python
Olaf - Olaf: Overly Lightweight Acoustic Fingerprinting is a portable acoustic fingerprinting system.