matchering VS pyAudioAnalysis

Songmastr is a web-based AI mastering tool. Utilizing the power of the open-source Python library called Matchering, Songmastr is able to create a masterful audio track that matches a reference song of your choosing. The algorithm studies the RMS, FR, peak amplitude and stereo width of your reference track before applying it to the target audio file.

Ever tried matching ? I really dig that tool: https://github.com/sergree/matchering

I made an automatic mastering website (www.songmastr.com) using the open source software Matchering (all credit to them). I have no users for the meantime, so I'd be happy to get some feedback ! How it works: 1 - You upload your song 2 - You chose a reference for mastering from the catalog / or you upload your own 3 - That's it ! Download the result. The matchering algorithm tries to match frequency response, RMS, peak amplitude and stereo width. The goal is not to replace professional mastering, but rather to offer a quick way to get your demo, or home recording in the same ballpark as commercial songs. Is it free ? - The link below gives out 5000 credits to the first users to claim them - After that, I plan on offering 2-3 free masters per week. Users that need more intensive server use can purchase credits Try it out: https://www.songmastr.com/?oip=eNoztTAx0zM1MjDRs7C0NNAzMjc2BgAmiwPW Have a nice day,

Re: Matchering (https://github.com/sergree/matchering), here is a little more information. If I recall correctly there is a docker-ized version so you can run it locally relatively easily if you are willing to learn a couple of bash commands. I have not played with it a lot and it is separate from the DAW.

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

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.

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.

To learn more about pyAudioAnalysis here you go.

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.

This might be relevant, which has a set of feature extraction methods implemented: https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction

A library for audio feature extraction, regression, classification, segmentation\ (2 comments)