I would like to thank you and appreciate for all the effort that you are taking to make these videos. I am a graduate student who is passionate about signal processing and machine learning in audio and your content is the best so far I have found. Since there are very less resources these are all the more helpful
Thanks very much for this series. Your code example and patient explanation really helped me a lot for getting started in the field of AI audio signal processing
Wow, thank you for putting this together! Though I'm a musician, I don't have a background in audio signal processing and it's been a struggle to find a good compiled source of information for deep learning specific to audio domain problems.
Thank you for this series chief; I'd been looking at trying to build a detection algorithm using a fourier transform to produce spectrograms and the theory refresher has been super super useful Plus confirmation that I wasn't barking up the wrong tree into how to approach it is always nice
Wow, I was watching the main series of Audio classification with Python you made. And when you explained the MFCCs passing as input to a CNN in your example you put this [100, 13, 1] as an input shape and you said the 100 came from the samples in the audio file (51200) / the hop length of 512 and I was about to ask where did the 51200 come from. Maybe with this series I'll be able to understand that and also the Mel Spectrogram which is something that I wanted to know about for the longest time from a non-so mathematical perspective, but rather a more code applicable one. Greetings to you and thank you very much
Thanks for these series. This is what I needed project that I had in my mind. Please don’t pull yourself back while going deep on theory :) I like to learn the theory along with practical examples.
Your channel sounds exactly what I was looking for as an ASR researcher. Thank you for these informative videos. Do you have tutorials on Montreal Forced Alignment too?
Very excited for this Valerio! Your videos have helped me immensely at understanding audio and ML/DL in general. Quick question: Do you have any recommendations or resources or plan to contribute some discussion about adding environmental noise to audio? I would love to talk about data augmenting with noise / other sound files into existing signals with proper RMS and SNR as to not overpower the desired signal. I'd love to see how to add radio static / random noises into audio for purpose of simulating target radio speech data. Thanks for everything!
I don't think I'll cover data augmentation in this series. Unfortunately, I don't a have a reference for noise adding. However, you can find papers which tangentially talk about it. I'm thinking of papers which, for example, introduce denoising autoencoder architectures.
Hey Mr Hope you doing good ! Please Can you help me ? How Can we use speech recognition to detect falling in elderly people ? Just another question how to combine audio with image to implement fall detection ?? Thank you
Can you please do a video on speech emotion recognition...I have already seen your speech recognition and that is not enough for emotion classification...but a good one
Hi Torsha, I may cover speech emotion recognition in the future. The closest problem I've tackled on this channel is genre classification n my DL for Audio series. You can re-use most of those concepts for your problem.
I want to know about how to do Classification of heart sounds and Improve accuracy. Is the method used for heart sound classification the same as for audio classification?
This series will mainly be focused on audio DSP. You can check out my series on DL for Audio to get an idea of some of the architectures you can use for audio classification.
Do you know if AI could make polyphonic audio to MIDI faster and better? And what about the restoration of very old and mangled recordings? I really want to try to work on this if I can but it feels like a steep curve.
Thanks! Sorry, but I'm not aware of any such book. If you'd like to get a few ideas about general audio / music signal processing, you can check out this video where I suggest a few books on the topic th-cam.com/video/jb5F4PfuRrw/w-d-xo.html Some of the ideas you'll learn there, can be re-used for time series data.
Hello, thank you for the tutorial. I am using java for developping android Apps and I am searching for an android library for sound comparison. I have tried "musicg" library but it gives bad results. Do you know any alternative ? thank you again
Amazing content and so well explained!! how can i implement this knowledge in speaker verification? I already followed the previous videos and created a project which so far tries to identify the spoken user but despite the fact that im getting 1.00 accuracy the system still fails to identify correctly. Can anyone help?
Hi George, thank you! Serendipitously, a thread on speaker verification just came out on TheSoundofAI Slack community in the #advice channel. I suggest you go check that out and join the conversation ;)
To simplify this more --> The model will extract feature and classify the audio in one of the three groups whether its [Good,Bad,AVG] i would like to know is making such kinda model is feasible ????
Thanks, brother, for your generousity! You deserve more than a subscription. I'm from a computer vision background your tutorial helped me a lot! Yet I can't able to join the soundofai community. Could you check the problem?
We are trying to develop a robot , can we use this topic for understanding how to develop algorithm for the robot to listen to un natural sounds etc (not speech recognition)
Can you try this link? th-cam.com/users/redirect?event=video_description&v=iCwMQJnKk2c&q=https%3A%2F%2Fjoin.slack.com%2Ft%2Fthesoundofai%2Fshared_invite%2Fzt-f71npumr-anli6W4QCuZ8UCj2gLoBkw&redir_token=QUFFLUhqbjBEMUY2clB3UDhsZklLN29feEdaN1h0RXBFd3xBQ3Jtc0tsVHFzaXFtT2hvRC12eEYwRVdNSDFyOWJQb2J2ZGw3SnhIOXY0UmEwY3FKNmFXZ3R5REpvaFVHbXRhVVFndWJRWjdXYV9rdkdIRUljWTRXckhlV19vVVQwZ1VSSEJ2VFk2SFBTT0hTamh1SG5wUkRZbw%3D%3D
Hello, very interesting subject and video. I have an hugely important request to adress you. Is it possible to train an A.I so that it can compare a reference signal (input source signal) with an output signal (that is the modified result of the source signal) so it can calculate an corresponding signal for the output signal to be the same as the input signal? It may not be super clear but basically, the functional chain is: -an input signal that is a source of audio content -an output signal that is the audio content (input signal) but processed in a way that distord it temporally (out of phase for some part of the signal) and in amplitude. -a closed feedback loop that send the exact copy of the distorded output signal to a core processing unit that contain the ai. This feedback loop sits right in between the input signal and output signal so that the AI can align (by injecting a new signal to the input signal, a compensating signal) almost immediately the output signal with the input. I'm a student and hobbyist and your reply could really be a greatly appreciated and precious contribution to my project Thanks
When I found your Channel I found a great treasure. When you choose a subject you are playing on my soul strings. Really I am grateful.
Thanks a lot Ara!
This is exactly what I was thinking of working on as a machine learning project. Thanks.
It took me 3*10*8 secs to subscribe to your channel because you are a lifesaver. exactly what I needed for my side project at this point.
I would like to thank you and appreciate for all the effort that you are taking to make these videos. I am a graduate student who is passionate about signal processing and machine learning in audio and your content is the best so far I have found. Since there are very less resources these are all the more helpful
Thank you Suyash -- I'm glad you're finding my videos useful!
Love it! I'm a vibration analyst, we listen to machines. Thank you for putting this together.
Glad you like it Ty!
Thanks very much for this series. Your code example and patient explanation really helped me a lot for getting started in the field of AI audio signal processing
Very much excited for this series
I have just finished the series and I am telling you if you started, go until the end. Thanks a lot Valerio
Wow, thank you for putting this together! Though I'm a musician, I don't have a background in audio signal processing and it's been a struggle to find a good compiled source of information for deep learning specific to audio domain problems.
I just found an incredible TH-cam channel. Thank you so much!
Thanks a lot for your great series, Valerio! This helps me a lot with my current signal processing project.
Thank you very much for the course... have been looking for something like this for a long time
Awesome, can't wait :)
I found you
tf you doin here dylan?
Awesome, you’re covering the topics that I need for my actual project. Thank you and keep it up
Thank you for this series chief; I'd been looking at trying to build a detection algorithm using a fourier transform to produce spectrograms and the theory refresher has been super
super useful
Plus confirmation that I wasn't barking up the wrong tree into how to approach it is always nice
A mine of information I found by chance 💪
You are amazing!. Super interesting topics including regarding how to publish a paper.
Thanks :)
what a great content, i really need this series, thank you for your effort to make this series
Wow, I was watching the main series of Audio classification with Python you made. And when you explained the MFCCs passing as input to a CNN in your example you put this [100, 13, 1] as an input shape and you said the 100 came from the samples in the audio file (51200) / the hop length of 512 and I was about to ask where did the 51200 come from. Maybe with this series I'll be able to understand that and also the Mel Spectrogram which is something that I wanted to know about for the longest time from a non-so mathematical perspective, but rather a more code applicable one. Greetings to you and thank you very much
Really a great resource here. Thank you! 👏🏻👏🏻👏🏻
Thank you so much for the series they are really amazing and organized!!
Waiting for next videos.....
Very interesting
Can't wait for it
I was desperately waiting for this series. 😀
Glad to hear that!
Can't thank you enough for this series.
Thanks for these series. This is what I needed project that I had in my mind. Please don’t pull yourself back while going deep on theory :) I like to learn the theory along with practical examples.
Love it already! ...looking forward to the next video...
Your channel sounds exactly what I was looking for as an ASR researcher. Thank you for these informative videos.
Do you have tutorials on Montreal Forced Alignment too?
Very excited for this Valerio! Your videos have helped me immensely at understanding audio and ML/DL in general.
Quick question: Do you have any recommendations or resources or plan to contribute some discussion about adding environmental noise to audio? I would love to talk about data augmenting with noise / other sound files into existing signals with proper RMS and SNR as to not overpower the desired signal. I'd love to see how to add radio static / random noises into audio for purpose of simulating target radio speech data.
Thanks for everything!
I don't think I'll cover data augmentation in this series. Unfortunately, I don't a have a reference for noise adding. However, you can find papers which tangentially talk about it. I'm thinking of papers which, for example, introduce denoising autoencoder architectures.
I am working on a project and man this is reall helpful. Thankew so much
new to the subject here with some (limited) dsp knowledge. Which of your playlists should i start with ?
This one is a great start.
it seems an execellent contente, i will start your course, i am really motivated and excited, thanks a lot
Hey Mr Hope you doing good !
Please Can you help me ? How Can we use speech recognition to detect falling in elderly people ?
Just another question how to combine audio with image to implement fall detection ??
Thank you
Can you please do a video on speech emotion recognition...I have already seen your speech recognition and that is not enough for emotion classification...but a good one
Hi Torsha, I may cover speech emotion recognition in the future. The closest problem I've tackled on this channel is genre classification n my DL for Audio series. You can re-use most of those concepts for your problem.
thank you Valerio, a lot of knowledge in this series. I'd donate superchat to this video but you don't have them enabled.
Thank you so much for this video!!!
💕💕💕 Thank you for the playlist.
This is an awesome series!
Thank you so much! This is very helpful to my project degree
what are the prerequisite fundamental courses for this series video?
i like this series before that i watched hhhhhhh nice introduction dude
If I could like a video twice, I would.
I want to know about how to do Classification of heart sounds and Improve accuracy. Is the method used for heart sound classification the same as for audio classification?
Can't wait for the next one!
It'll be next week, first thing on Monday :)
Great stuff, thank you for your effort.
Hi Valerio,
I am planning to extract feature from a neural series like EMG or EEG. Can the same techniques be applied for neural series signals?
Yes, several of the techniques I discuss can be applied to EMGs/EEGs.
Please cover Discrete Wavelet transform (DWT) feature extraction method
YOU SAVED MY FYP !
How could i get in touch with you?
Just 👏in 👏time 👏
Excellent content, keep it up! :D
I want to know about how to do Classification of heart sounds and Improve accuracy
This is good stuff 👍
I subscribed you when you said i love python
Good luck sir
Please can u also add a video on basic sound detection and algorithm used to do that...
This series will mainly be focused on audio DSP. You can check out my series on DL for Audio to get an idea of some of the architectures you can use for audio classification.
Could you please share about speech analyzer like elsa ?
I am waiting for it,sir.
Hello are the next videos of the playlists obsolete?
No.
@@ValerioVelardoTheSoundofAI thanks you for the free courses
Is there any udemy or coursera or edureka courses on audio files processing? Suggestion please
Do you know if AI could make polyphonic audio to MIDI faster and better? And what about the restoration of very old and mangled recordings? I really want to try to work on this if I can but it feels like a steep curve.
great playlist!! .....can you suggest me like how toapply wavelet transform to sudio signal using python ?? is there any way ?
This is great content! Thank you very much. Is there a book that you would advise someone who wants to learn signal processing for time series data?
Thanks! Sorry, but I'm not aware of any such book. If you'd like to get a few ideas about general audio / music signal processing, you can check out this video where I suggest a few books on the topic th-cam.com/video/jb5F4PfuRrw/w-d-xo.html Some of the ideas you'll learn there, can be re-used for time series data.
Thanks alot
Hello, thank you for the tutorial. I am using java for developping android Apps and I am searching for an android library for sound comparison. I have tried "musicg" library but it gives bad results. Do you know any alternative ? thank you again
Hey brotha is there a way I can send you this audio I captured and can you tell me what’s really going on
Amazing content and so well explained!! how can i implement this knowledge in speaker verification? I already followed the previous videos and created a project which so far tries to identify the spoken user but despite the fact that im getting 1.00 accuracy the system still fails to identify correctly. Can anyone help?
Hi George, thank you! Serendipitously, a thread on speaker verification just came out on TheSoundofAI Slack community in the #advice channel. I suggest you go check that out and join the conversation ;)
Hi
Could you help me
How to find abnormalities from audio file
Is it possible from ML?
can you make a tutorial for Kaldi
one question, is calculating human singing potential is feasible by use of deep learning ??
To simplify this more --> The model will extract feature and classify the audio in one of the three groups whether its [Good,Bad,AVG]
i would like to know is making such kinda model is feasible ????
We are working for an project Covid 19 and acoustics. Do you have any suggestions for us?
You can check out this video I made on the topic th-cam.com/video/Skzuva3chIM/w-d-xo.html
Thanks, brother, for your generousity! You deserve more than a subscription. I'm from a computer vision background your tutorial helped me a lot! Yet I can't able to join the soundofai community. Could you check the problem?
I've tried out the link, It seems to work for me. Can you let me know what kind of issue you're experiencing?
We are trying to develop a robot , can we use this topic for understanding how to develop algorithm for the robot to listen to un natural sounds etc (not speech recognition)
Yes, you'll find the foundations for what you need here.
can you make Audio classification using efficientNet. it would be better for us to understand efficient net
Penso che tu sia italiano. Io sono un linguista, mi occupo di fonologia. Grazie per il tuo lavoro!
Grazie Giuseppe!
please can you make a tutorial series on speech synthesis using cnn and thank you for this wonderful content.
Thanks! I've just started a series on sound generation with variational autoencoders.
Hi... Do you have a similar course material for learning speech recognition using Hidden Markov Models (HMM)?
No, I don't.
love it
you
are
*amazing* !
Can you talk about Google Magenta's Differentiable Digital Signal Processing and how it works? I think it is too underrated tbh.
That's an amazing application. I won't touch on it in this series, but definitely cover the subject in a video at some point.
Where my fellow Electrical Engineers at?
Hereeeeeeeeeeeee
cool
I have tried signing in thesoundofai.slack.com ,however got reply as email id not exist with slack.com
Can you try this link? th-cam.com/users/redirect?event=video_description&v=iCwMQJnKk2c&q=https%3A%2F%2Fjoin.slack.com%2Ft%2Fthesoundofai%2Fshared_invite%2Fzt-f71npumr-anli6W4QCuZ8UCj2gLoBkw&redir_token=QUFFLUhqbjBEMUY2clB3UDhsZklLN29feEdaN1h0RXBFd3xBQ3Jtc0tsVHFzaXFtT2hvRC12eEYwRVdNSDFyOWJQb2J2ZGw3SnhIOXY0UmEwY3FKNmFXZ3R5REpvaFVHbXRhVVFndWJRWjdXYV9rdkdIRUljWTRXckhlV19vVVQwZ1VSSEJ2VFk2SFBTT0hTamh1SG5wUkRZbw%3D%3D
please send code for this
Check the description box.
GOLD MINE
Hello, very interesting subject and video.
I have an hugely important request to adress you.
Is it possible to train an A.I so that it can compare a reference signal (input source signal) with an output signal (that is the modified result of the source signal) so it can calculate an corresponding signal for the output signal to be the same as the input signal?
It may not be super clear but basically, the functional chain is:
-an input signal that is a source of audio content
-an output signal that is the audio content (input signal) but processed in a way that distord it temporally (out of phase for some part of the signal) and in amplitude.
-a closed feedback loop that send the exact copy of the distorded output signal to a core processing unit that contain the ai.
This feedback loop sits right in between the input signal and output signal so that the AI can align (by injecting a new signal to the input signal, a compensating signal) almost immediately the output signal with the input.
I'm a student and hobbyist and your reply could really be a greatly appreciated and precious contribution to my project
Thanks
You do not work with MATLAB? I'm not using machine learning. Will this help me?