Skip to main content

This derives from my PhD thesis: Deng, J., Large Vocabulary Automatic Chord Estimation from Audio Using Deep Learning Approaches. PhD thesis, Department of Electrical and Electronic Engineering, The University of Hong Kong, 2016

However the code of this work is written in matlab (https://github.com/tangkk/tangkk-mirex-ace). So I try to port those code into python. But this is by no means a direct porting. It is meant to be minimalist version of ACE, which keeps only the algorithmic gist of the original work.

Compared with the original version which supports sevenths chords and inversions, this piece of code currently only supports maj and min triads, and it has much more elegant (only a few lines of) feature extraction and segmentation codes, which leverages librosa and hmmlearn.

Metadata

Release files for pyace 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyace 0.1.1
File Size Uploaded
pyace-0.1.1.tar.gz 11.4 kB Details

Release files / pyace-0.1.1.tar.gz

Download URL pyace-0.1.1.tar.gz
Size 11.4 kB
Tags Source
SHA-256 checksum
How to use checksums
865216f32aabeaa66eada205b03cabde32d01137dd1a3cc05e1c377f1cc7dbf4
BLAKE2b-256 checksum
How to use checksums
84f5379d9f221e6c65d67687423677b00696d35a51e85931bc91614fc2b87372
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release history Release notifications | RSS feed

This release

0.1.1 This release

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page