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Leo_D517 OP t1_jd2g6pg wrote

Reply to comment by CheekProfessional146 in [Project] Machine Learning for Audio: A library for audio analysis, feature extraction, etc by Leo_D517

First, librosa is a very good audio feature library.

The difference between audioflux and librosa is that:

  • Systematic and multi-dimensional feature extraction and combination can be flexibly used for various task research and analysis.
  • High performance, core part C implementation, FFT hardware acceleration based on different platforms, convenient for large-scale data feature extraction.
  • It supports the mobile end and meets the real-time calculation of audio stream at the mobile end.

Our team wants to do audio MIR related business at mobile end, all operations of feature extraction must be fast and cross-platform support for the mobile end.

For training, we used the librosa method to extract CQT-related features at that time. It took about 3 hours for 10000 sample data, which was really slow.

Here is a simple performance comparison

Server hardware:

- CPU: AMD Ryzen Threadripper 3970X 32-Core Processor
- Memory: 128GB

Each sample data is 128ms(sampling rate: 32000, data length: 4096).

The total time it takes to extract features from 1000 sample data.

Package audioFlux librosa pyAudioAnalysis python_speech_features
Mel 0.777s 2.967s -- --
MFCC 0.797s 2.963s 0.805s 2.150s
CQT 5.743s 21.477s -- --
Chroma 0.155s 2.174s 1.287s --

Finally, audioflux has been developed for about half a year, and open source has only been more than two months. There must be some deficiencies and improvements. The team will continue to work hard to listen to community opinions and feedback.

Thank you for your participation and support. We hope that the follow-up of the project will be better and better.

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waffles2go2 t1_jd5su7l wrote

> FFT hardware acceleration based on different platforms

???? I love me some FFTs but "hardware acceleration"?

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