Music, Music related events, Audio Coding, Signal Processing, Audio Programming, Music Technology, Machine Learning, Deep Learning, Music Information Retrieval
Just watched this video. This is indeed a great tutorial video. I am about plan a listening test for a project of mine. I am sure I have some great pointers here. Thanks for sharing.
Hi , I was going through your material. It is great content. I am currently looking for a program that parses midi from guitar tabs. Do you have any material on that topic ?
Hi, thanks for your comment. Unfortunatelly I don't have anything from tab to midi, but just the basics of automatic music transcription ru-vid.com/group/PL6QnpHKwdPYh5OdFSt7kNqIPFEvXJRiUQ (from audio to notation and midi, only for basic monophonic audio )
Já viu a versao extendida do video do evento com partes que o youtube desmonetizou? ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-OMLX-rRZg7w.htmlsi=IyaqIEOnoDPj-ar2
@@Guitars4RL vou assistir logo já! essa banda final de hair metal me encantou muito, além das meninas do Honey Bomb!! vou conferir na íntegra com ctz! obrigado meu amigo! Stay Rock! 🤘❤️💪🎸
I do recommend, I visited it only once. It was free to enter (though some nights there's an entrance fee), they have live music every night, beer was good and price was ok.
Amsterdam is a nice place to visit but it really depends on your personal taste. There's also the Bourbon Street but I didn't get to visit yet, it's on my list www.bourbonstreet.nl/
For me, when I try to run python3 install.py --sys-prefix, it appears that I don't have the permission to do so. python3 install.py --sys-prefix install.py:164: DeprecationWarning: replace is ignored. Installing a kernelspec always replaces an existing installation install_dest = KernelSpecManager().install_kernel_spec( Traceback (most recent call last): File "install.py", line 164, in <module> install_dest = KernelSpecManager().install_kernel_spec( File "/home/joao/.local/lib/python3.8/site-packages/jupyter_client/kernelspec.py", line 406, in install_kernel_spec shutil.copytree(source_dir, destination) File "/usr/lib/python3.8/shutil.py", line 557, in copytree return _copytree(entries=entries, src=src, dst=dst, symlinks=symlinks, File "/usr/lib/python3.8/shutil.py", line 458, in _copytree os.makedirs(dst, exist_ok=dirs_exist_ok) File "/usr/lib/python3.8/os.py", line 213, in makedirs makedirs(head, exist_ok=exist_ok) File "/usr/lib/python3.8/os.py", line 213, in makedirs makedirs(head, exist_ok=exist_ok) File "/usr/lib/python3.8/os.py", line 223, in makedirs mkdir(name, mode) PermissionError: [Errno 13] Permission denied: '/usr/share/jupyter' Can you please help me out here?
Unfortunately this video is 3 years old and a lot has changed in Colab. I don't know what are the latest versions pre-installed in the actual version of Colab and all the conflicts it exists by now :-( maybe you can try to install in your local system using the older versions from that time.
Ce musee fe paris doit correspondre avec des obelisque et des metalurgique de eiffel et non cette des instrument a corde qui sont insignier par leurs fornication et leurs abomination des instrument a corde d arabie saodite
Hi, It seems that to derive filter coefficients, you are multiplying the matrix A computed from noisy signal y using the original (clean) signal x. My question is how is the filter useful in practice where you will not have the clean signal x?
It is described around 17:58. You have to estimate the auto-correlation of the noisy signal and the cross-correlation between the noisy and clean signal to get the filter coefficients. It comes down to knowing the power spectra of noisy and clean signal and generating the correlation signals from them using inverse fourier transforms. So you most importantly need an estimate of the spectrum of your clean signal, then the ideal wiener filter coefficients can be derived from that.
Pyroomacoustics has a lot of limitations. My first suggestion would be to go through the documentation and some times even the source code t check if it supports your use case.
