I went through a similar career change so I can totally relate. You’ll never regret learning python. It’s so powerful!

@@robmulla I found my place in the world. thamks a lot!!!!

I need to create a cell phone application that compares two audios, one previously recorded and the other spoken at the time, and I need the application to say if they are the same, is there any reference work that you can point me to?

How can machine learning and AI change your previous industry ?(audio/sound engineer)

@@EtBilu295 Shazam algorithm, check it out

Love this feedback. Glad to hear this helps you start your python journey. It’s such an awesome language. The others are good too!

So the reason why devs say things like this is because Python is rarely used to actually put out a full fledged application that's essentially IOT compatible; then there's the speed. C++ is apparently faster so everyone wants you to learn that.

Thanks for the feedback Filippo, glad to hear you learned something new. Cool to know you're watching from Europe maybe I'll do a twitch stream at a different time in the future and you can join!

Thanks Hadi. I'm glad you found it helpful.

Very true. Signal is signal. Glad you found it helpful.

Glad you found it helpful!

Awesome! I love it.

That’s a good suggestion. I’ve made models like that before but no video tutorial yet. I’ll put it on my todo list. Thanks for the suggestion

Thanks so much. I’m glad it helped you out.

Nice work! Do you have the code you could share?

@robmulla @ErickCalderin Could you please help me in my acedemic project. I am getting many errors while implementing speech emotion recognition project using cnn. Please guide me to complete my project

Hey Jerryl. I have plans to do that but I'm not sure when. Hope you liked the video.

Thanks Larry! I learned a lot of what I know through school and kaggle. Thanks for the congrats on the 1k twitch followers, hopefully just the beginning.

Thanks for watching and giving feedback!

I appreciate that Justin. Glad you liked it.

Thanks. That’s a great idea for a future video.

Did you get answer? I reall wonder what's the difference between them

That’s a good idea. Thanks for watching

I guess I’m just what you needed!

Thanks so much for the feedback.

Thanks. That’s a great question. It would depend on how accurate you would need the system to be and the volume of the voice. You could setup a heuristic to determine if there is voice based on the frequencies. You could also train a machine learning model as a classification problem. This would require a lot of labeled examples. There also may be some pre-trained models you could use.

@@robmulla Thank you for your response. Do you have any recommendations on pre-trained models I could explore? Or videos to watch? I don't need anything precise, but want to find out (roughly) what proportion of a corpus of audio contains human speech (as opposed to silence, music, other noise).

I’m not sure exactly but I know librosa has some modules for pulling on notes.

Hey, thanks for the comment. This sounds like an interesting project. I'd be a bit concerned with using something like this for such a serious medical condition like sleep apnea. Definately take the advice of a medical professional. But for a side project I think doing auto detection might be possible. You would need to deal with streaming audio which I don't cover here. I'm not sure what the best approach would be but wish you the best of luck and hope your sleep apnea gets better!

Hey Trevor, that's a great question. I mainly just code in python so I can't really say which is better. At least for machine learning I believe most of what you want to do can be accomplished with python. Hope that helps!

Thanks for the suggestion. I’ll add it to the list of potential future videos!

Thanks for watching.

Why not create a conda or virtual environment with python 3.7?

That's a great question. There are probably many different ways to do this. One way would be adding a filter to the audio file to remove the specific frequencies containing noise. Check out the librosa filters and try some of them out.

Glad you liked the video. Welcome to the wonderful world of python. You will love it.

Great question OrangUtan - the y-axis values in that plot is in decibels (dB). Decibels are not an absolute measure of sound energy but a comparison with a reference value. In the librosa function "power_to_db" you can see it takes a reference value, which we provide as the max value. Since dBs are in log scale all negative numbers are less than this max value. Hope that helps! Check this out for more info: www.scienceabc.com/pure-sciences/why-negative-decibels-are-a-thing.html

@@robmulla hi rob thanks for the answer. another question, since you mention about "power_to_db" what is the difference with function "amplitude_to_db" 18:27?

That’s a good point. I don’t really know. Have you figured it out? I’m curious to know.

Thanks. I used a green screen sheet with OBS software to overlay my camera in front of the screen.

@@robmulla Thanks. Please check your twitter messages, I've sent you a PM there

I’d recommend checking out my introduction to jupyter and jupyter notebooks video. It explains how to write python code in a notebook. kzfaq.info/get/bejne/a9aWY8KT1bTGm6s.html

@@robmulla many thanks will do!

Glad you enjoyed the video. I'm running on linux, but librosa usually installs fairly easily for me. I use conda/anaconda as my base python environment. I then just pip installed librosa. Other than that I'd suggest reading the install instructions on their documents page: librosa.org/doc/latest/install.html

@@robmulla thank you so much for the info and prompt response. I think my African Bantu Language project has a chance with the milestones.

Good question. This is pretty comment for deep learning. Check this notebook: www.kaggle.com/code/colinnordin/audio-segmentation-tutorial

@@robmulla Thank you for your response.

Although I’m not a fan of excel, this should be pretty easy. Simply wrap the DB transformed dataset like this df = pd.Dataframe(y) and then save the data as CSV df.to_csv(‘my_data.csv’)

@@robmulla alright. Thank you so much.

Thanks for watching 😆

Hey Damon, not sure exactly what you are looking to do but should be pretty easy to save a trimmed audio file using the techniques I show in this video. You would just need to also save the result.

Thanks, will do!

Great question. In this part of the video I am just zooming into part of the audio to show how it looks visually in the plot. Each datapoint has a relatioship to time by the "sample rate" - this audio file has a sample rate of 22050 Hz (22050 samples / second). so 1 sample is 1/ 22050 of a second. This slice of 500 samples equates to ~0.0227 seconds. Hope that helps!

@@robmulla thanks a lot !

Glad you think so. Thanks for watching.

Absolutely, a melspectogram is just like that - time x feature!

@@robmulla if i want to use lstm model, suppose i have a video, i have to trim multiple short videos and convert it into audio, then use librosa transformation for these short videos and convert all transformations into time series? Or is it possible with single audio file to split data into frames?

​@@robmulla if my loaded audio data shape is (67032,) can i reshape it to (12,5586) #feature size and then repeat data with 3 time step to create (3,10,5586)

There are many. "Approaching (Almost) Any Machine Learning Problem" by my friend Abhishek is a good one but also very technical.

I had a whole spiel about how the sample rate is dictated by the Nyquist theorem but it answered none of your question so I decided it best not but I am also curious how it derived that sample rate seeing as it appears to be half of 44.1 but I'm not certain why it would give it in that form

gz is usually a gzipped file. So you need to unzip it first.

Glad you are learning something new. At that timestamp I am only looking at a single audio file results. `audio_files[0]` means I am only reading the first value in the audio_files list.

@@robmulla thanks for reply

I'm not sure exactly what you mean but there are many ways of doing feature engineering with librosa. Check this out: librosa.org/doc/main/feature.html

Glad you liked it!

Glad you like it!

Great question. Check out the filters in scipy.signal. There are different filters you can apply that should work similar to an equalizer. Good luck!

Thanks for your question. That sounds like an interesting project but it's well beyond the scope of what I can help you with here. Good luck.

That seems like a cool project. I can’t help directly but you can join my discord and ask there! Glad this video helped you get started

it's probably something with how you imported it but I cant say without seeing your code.

@@robmulla is it possible somehow to send you ,it'll be a great help for me

Hey Danish. Thanks for watching the video. When you says "different dbs" do you mean change the volume of the audio? If so you might want to check this package out: pydub.com/

I'm not an expert on PCM data, but if you are able to convert it into a numpy array you should be able to save it as a wav file using librosa. Maybe this could help: stackoverflow.com/questions/16111038/how-to-convert-pcm-files-to-wav-files-scripting

@@robmulla With PCM data I mean something like you showed in your intro, 16 kHz audio samples with 16 bit resolution, we capture sound with a simple ESP32 + I2S microphone and the ESP32 streams the samples over a websocket to any other websocket. Is a numpy array different then a normal array or list? Is there an example in one of your tutorials how to put integer sample values into the numpy array and convert it to a wav file? Can this be done also in real time? What I like to do is to record the streamed sound and analyze it later ( or real time if possible) and count how many times a particular sound occurs in the recording. All advise is very welcome! ( technical college project about AI and sound pollution ) Thanks.

Great question Julien. SHIFT + TAB. In jupyterlab it will show the docstring for the function you are calling. This also works in a kaggle notebook like I'm using in the video. I cover this and more in my jupyter notebook tutorial video.

Not sure exactly what you are asking. By "train" do you mean record?

@@robmulla No, I have voice data, how can I create a model from this voice data, I will use it to convert text to speech

For all frequencies? You could try librosa.effects.trim which will remove silence below a threshold. Then compare with the original and see which timestamps are removed. Good luck!

@@robmulla how to compare them, i m a total noob 😂

Great question. From what I've seen they are almost identical. There are some differences with pylab being preferred for non-interactive plots.This post goes into more detail: stackoverflow.com/questions/23451028/matplotlib-pyplot-vs-matplotlib-pylab

Thanks @@robmulla

Thanks so much for the sub!

What video do you need. I'm confused about your comment :)

Sorry - did you have a question?

Will do thanks.