Speech emotion recognition
₹1500-12500 INR
Kiszállításkor fizetve
Abstract
Emotion recognition or affect detection from speech is an old and challenging problem in the field of artificial [login to view URL] significant research works have been done on emotion Recognition by using different approaches as it. can be useful in the field of
medical science , robotics engineering, call center application etc. Human can easily recognize emotion of speaker but the question is machine can?
Flow of the System
Identification of emotion can be done by extracting the features or different characteristics from the speech and a training is needed for a large number of speech database to make the system accurate. The steps towards building of an emotion recognition system are, an emotional speech corpora is selected or implemented then emotion specific features are extracted from those speeches and finally a classification model is used to recognize the Emotions.
Tentative Project Module Names with Description:
1 ) Data Processing
Gather the libraries and test their functions such as
import tensorflow
import keras
import pickle #To store our model
Load the data into diffrent folders containing angry and neutral audio files.
2) Featurization
Before going into pre-processing and data exploration to select our features.
Mel scale — deals with human perception of frequency, it is a scale of pitches judged by listeners to be equal distance from each other.
Pitch — how high or low a sound is. It depends on frequency, higher pitch is high frequency.
Frequency — speed of vibration of sound, measures wave cycles per second.
Chroma — Representation for audio where spectrum is projected onto 12 bins representing the 12 distinct semitones (or chroma). Computed by summing the log frequency magnitude spectrum across octaves.
Fourier Transforms — used to convert from time domain to frequency domain. Time domain shows how signal changes over time.
Expected Outcome : How much of the signal lies within each given frequency band over a range of frequencies
3) Model Training
We will begin with a simple model architecture, consisting of three layers - an input layer, a hidden layer and an output layer. All three layers will be of the dense layer type which is a standard layer type that is used in many cases for neural networks.
We will also apply a Dropout value of 50% on our first two layers. This will randomly exclude nodes from each update cycle which in turn results in a network that is capable of better generalisation and is less likely to overfit the training data.
For compiling our model, we will use the following three parameters:
Loss function - we will use categorical_crossentropy. This is the most common choice for classification. A lower score indicates that the model is performing better.
Metrics - we will use the accuracy metric which will allow us to view the accuracy score on the validation data when we train the model.
Optimizer - here we will use adam which is a generally good optimizer for many use cases.
After that We will start with 100 epochs, which is the number of times the model will cycle through the data. The model will improve on each cycle until it reaches a certain point.
Lastly, We will store our model in a pickle file, so we don't have to keep training it again and again while testing.
4) Testing:
Extract the features, then print the prediction in percentage.
5) Visualization and data Storage.
User will be given options to upload a audio file then our algorithm will tell the particular mood with the percentage and will give graph of emotions. This all data will be stored in a database(sqlite3).
Projektazonosító: #28406061
A projektről
14 szabadúszó tett átlagosan 9917₹ összegű árajánlatot erre a munkára
Hi, I hope you are doing fine. I have almost 10 years of experience in machine learning algorithms. I can implement various types of artificial intelligence algorithms including yours with Matlab, Python, JAVA and etc. Továbbiak
Hi, I have +5 experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day
Hy , hope you are doing well I can help you to do any kind of machine learning work related to speech emotion recognition. As i have already worked on speech recognition and speech conversion. There are so many featur Továbbiak
Hello I am very interested in your job as speech processing engineer, who has 7+ years of R&D experience in speech/speaker recognition, speech emotion recognition, lung sound classification. I've ever deployed speech e Továbbiak
Hai... i will do your work in python. i will consider 4 emotional speech database like saveee,tess,crema-D and radvess. i have read your description fully. sure I will do. kindly text me thankyou so much.
hy i am expert in MATLAB ,Artificial intelligence and Detection i believe that i am perfect for this task i have alredy done many task of thus kind lets have a discussion thanks
I've 2 year experience in Python and Machine Learning. Your project will be done perfectly within the deadline provided.
Dear client, Your project caught my eye because of the challenge it offers. I understand finding the right fit for your project is top priority. A project like this really need someone with good command over deep lea Továbbiak
I have already built a emotional recognition engine using BI-LSTM and Attention Mechanisam. I can do your project maximum in two days. Looking for your reply