Speech Emotion Recognition (Sound Classification) | Deep Learning | Python

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⭐️ Content Description ⭐️
In this video, I have explained about speech emotion recognition analysis using python. This is a classification project in deep learning. I have build a LSTM neural network to build a classifier.

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🕒 Timeline
00:00 Introduction to Speech Emotion Recognition
03:51 Import Modules
06:20 Load the Speech Emotion Dataset
12:34 Exploratory Data Analysis
25:20 Feature Extraction using MFCC
38:20 Creating LSTM Model
45:37 Plot the Model Results
49:15 End

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Комментарии

Hi Everyone!!! New video with transformers has been released (99.6% accuracy) and link is available in description with latest code.

For the current video, due to some hidden files in kaggle data, the dataset has been loaded 2 times. Please check the github repo for the updated code file. The validation accuracy will be around 67% which is better for a baseline multiclassification. The process remains the same for the project.

Due to module updates, there are few changes needs to be done. It has been updated in github notebook.
1. sns.countplot(data=df, x='label')
2. In waveplot function, update- librosa.display.waveshow(data, sr=sr). Use waveshow instead of waveplot in librosa

Happy Learning!!!!

HackersRealm

Hii 👋🏼! I wanted to follow this for recognising baby's cry using their tone of sound . If I used required data set for it will I be able to do it using this.

rpvikas

Thank you so much from my heart I am searching this project from past week some are paid and some didn't share the code you are amazing❤❤❤

alisher

The video, I was finding the same after searching a lot at last I get it in this video all the content I needed those all contents are mentioned and for what I was searching. I am satisfied with this video. 🥰😇

radhebhay

Hey brother, how do i get the final output, that is the final emotional classification eg happy sad anger
Please tell!!!

Storm_cipher

What are the train and test set in this case?

LamNguyen-hwlq

Thank you for your code. Could you please add how can I test the model with a new audio file?

laurapagani

Thanks for this tutorial.
I am interested in implementing personality trait speech recognition, which I think is somehow related to emotion recognition. Any idea/advice on this topic? Thank you!

bakhshizade

Thanks for the detailed explanation, just some questions as I'm new to this.
So the models are using features like mfcc for recognition, that means the waveplot and spectrogram is for visual representation only. right?,
Also how can I make changes so that I give the audio file as input and the model gives emotion as output?

DivyaPrakashMishra

Thanks you so much for your tutorial but can you please tell how this helps to extract stress score.

PrinceGhotar

which features you took?
only mfccs? what about energy, pitch, zcr, other features too?

c.mirashi

Am I correct to assume that because the training acc and validation acc are not far from one another then the model is not overfitting and performing well?

If the validation acc is less than the training acc then it is overfitting, right?

Thank you for your answers.

benedictespiritu

Please make a video on how to deploy this model on a web application using flask. That will be great!

sarfarazahmed

Hello sir the explanation was awesome but i a getting lot of error can i get some help

MSRRAJPUT-yxku

Hi! I've followed the tutorial from top to bottom. If you do it with the same way in the tutorial, you get a close to 100% val_accuracy, but that's only because the training image and the testing image are the same. If you do it with half the image, like in the Github files, you get a around 25% val_accuracy, which is a lot more realistic but really ineffective. Is there any way to improve the val_accuracy without "cheating" and using the same data for testing and training?

Raysworkshop

Hello, thanks for the tutorial! Can the model be used for real-time emotion recognition of a random voice?

LitalLevy-mq

Sir amazing project with beautiful explaination
just one question why LSTM ? we all know they work good with textual data

sagarbhagwani

hey brother, excellent video, currently I run it in Jupyter notebook and if it turned out very well, I need some advice, if I want to try it with a different audio, how can I do it?

id-wilgenjacobo

Thanks for the video, but after the model is trained at the end, which label is the trained model?
feat, angry vs.
This stage is important for me, would you be interested?

eminyazilim

can this model is also okay if we take the input from the microphone and then it will compare it with the dataset the classify emotion

sabahatmutto

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