[HN Gopher] Spectrograms - audio signal processing for machine l...
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Spectrograms - audio signal processing for machine learning
Author : sebg
Score : 46 points
Date : 2021-11-05 00:11 UTC (1 days ago)
(HTM) web link (medium.com)
(TXT) w3m dump (medium.com)
| l33tman wrote:
| Just noting that this is long and _incredibly_ basic. It doesn 't
| cover anything about spectrograms for example despite the subject
| title.
| ganzuul wrote:
| There needs to be experiments with representations such as
| chirplets since those are actually physically relevant.
| [deleted]
| stereosteve wrote:
| The Audio Signal Processing for Machine Learning [1] youtube
| playlist is a pretty excellent course on sound processing for ML.
| In particular, the MFCC video. [2]
|
| [1] https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-
| wATfeyAM...
|
| [2] https://www.youtube.com/watch?v=4_SH2nfbQZ8
| TrackerFF wrote:
| FWIW, if you want to dip your toes into audio and song
| classification, the Spotify API offers feature extraction.
|
| Also, there are some very cool ML topics in the frequency domain.
| For example - in the past years there's been a small revolution
| in the guitar / bass / etc. industry where simulators offer quite
| realistic sounding amps/speakers sounds.
|
| Back in the day, these simulators were just carefully crafted
| filters that would emulate the response of said amps, speakers,
| room/ambience, etc. but with the advent of more powerful DSP, it
| is now possible to sort of "extract" these features on the fly -
| some of these products do indeed use ML/DL algorithms in their
| core. In the end, the goal is to profile the environment, and
| create some complex filter / function that matches the response.
| Which is one thing Neural Networks can do quite well
| (approximating functions).
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(page generated 2021-11-06 23:02 UTC)