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    • ?
      A Former User @hisefilo
      last edited by

      @hisefilo said in Dynamic EQ Yet?:

      innovative product ideas.

      I think ML might have some potential for innovation - I managed to turn a tree-smack into a taiko from a few minutes of training using Magenta

      unfortunately it's mono and only 16khz so there's some work left to be done :P

      before:

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      hisefiloH ? 3 Replies Last reply Reply Quote 1
      • hisefiloH
        hisefilo @A Former User
        last edited by

        @iamlamprey said in Dynamic EQ Yet?:

        think ML might have some potential for innovation -

        Absolutelly. I think this kind of things are the best marketing you can invest your time in

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        • hisefiloH
          hisefilo @A Former User
          last edited by

          @iamlamprey said in Dynamic EQ Yet?:

          tree-smack

          I always dreamed with the idea of a ML algo that can learn articulations from real players. Also learning from styles.... Like if you can choose to play a trumpet into a Miles style

          ? 1 Reply Last reply Reply Quote 0
          • ?
            A Former User @hisefilo
            last edited by

            @hisefilo I'm sure it's possible, just need someone infinitely smarter than me to develop it 😂

            my ultimate plugin would be a 5mb vst that the end user opens, types "aggressive guitar" or "soft piano" and gets an ML-derived physical model with unlimited expressivity...

            hisefiloH 1 Reply Last reply Reply Quote 0
            • hisefiloH
              hisefilo @A Former User
              last edited by hisefilo

              @iamlamprey I think you are smart enough. I i know nothing about ML. But I guess you can train a model to classify audios depending on spectal data? I mean a ppp C4 note from trumpet will always look similar, no matter the trumpet

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              • ?
                A Former User @hisefilo
                last edited by

                @hisefilo I'm only smart enough to use someone else's model, writing my own would result in my head exploding at least 3 times

                But I guess you can train a model to classify audios depending on spectal data? I mean a ppp C4 note from trumpet will always look similar, no matter the trumpet

                I believe a guided model could be taught what a ppp trumpet C4 sounds like (and every other note), with a big enough model on the actual instrument, you could then teach that new model the disparities of different players... in theory so...

                "now that we know what the instrument sounds like, here's it being played by different musicians"

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                • lalalandsynthL
                  lalalandsynth @d.healey
                  last edited by

                  @d-healey Yes, optimize your website and work hard on the SEO.
                  That is the cheapest marketing you can get and effective.
                  And yet us little guys seem to ignore it a lot of the time.

                  This is what I am doing with my synth website these days , its a lot of boring work but worth doing.

                  https://lalalandaudio.com/

                  https://lalalandsynth.com/

                  https://www.facebook.com/lalalandsynth

                  https://www.facebook.com/lalalandsynth

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                  • ?
                    A Former User @A Former User
                    last edited by A Former User

                    @iamlamprey said in Dynamic EQ Yet?:

                    @hisefilo said in Dynamic EQ Yet?:

                    innovative product ideas.

                    I think ML might have some potential for innovation - I managed to turn a tree-smack into a taiko from a few minutes of training using Magenta

                    unfortunately it's mono and only 16khz so there's some work left to be done :P

                    before:

                    Your browser does not support the audio element.

                    after:

                    Your browser does not support the audio element.

                    Did you use NSynth? Machine learning is a really interesting subject. I’ve been diving into it as well in recent weeks…

                    ? 1 Reply Last reply Reply Quote 0
                    • ?
                      A Former User @A Former User
                      last edited by

                      @UrsBollhalder I used DDSP from Magenta:

                      Link Preview Image
                      GitHub - magenta/ddsp: DDSP: Differentiable Digital Signal Processing

                      DDSP: Differentiable Digital Signal Processing. Contribute to magenta/ddsp development by creating an account on GitHub.

                      favicon

                      GitHub (github.com)

                      Starting with training a model using the autoencoder:

                      Link Preview Image
                      Google Colab

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                      (colab.research.google.com)

                      Then bringing that into the timbre transfer demo

                      Link Preview Image
                      Google Colab

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                      (colab.research.google.com)

                      The resynthesis tools in DDSP were exactly what I was looking for in terms of realistic synthesis of sounds - it uses a combination of Additive Synthesis, Subtractive Noise and deconvolution and sounds pretty good

                      The main issues I came across were the samplerate limitation (16khz) and the fact that it only train as mono signals, I'm not well-versed in python so I couldn't figure out how to fix those two things...

                      ? 1 Reply Last reply Reply Quote 0
                      • ?
                        A Former User @A Former User
                        last edited by

                        @iamlamprey Yes. I've been going through those repos as well! 😂
                        How much time did it take you to compute those algos? I am not sure why they use the reduced sample rate etc. Probably to make it more accessible for web audio use! Upping the quality will probably just influence to time it'll take to compute things! Amazing stuff!!!

                        ? 1 Reply Last reply Reply Quote 0
                        • ?
                          A Former User @A Former User
                          last edited by

                          @UrsBollhalder It was pretty fast, I used about 10 mins of samples, which I left running overnight so about 12-14 hours total for a relatively accurate resynthesis.

                          I think the 16k samplerate is for the actual training, once you start going to 44.1 or something higher like 96k the training would take a lot longer...

                          Probably the same thing for mono vs stereo (half the channels = half the time)

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