Infant Cry Classification Through Audio Signal
Objective
The objective is to classify infant cries into categories like hunger, pain, or anger, using an Artificial Neural Network (ANN) with spectrogram features extracted via Short Time Fourier Transform (STFT) and optimized using PCA to enhance accuracy.
Abstract
There exists a much need for classifying the infants cry for assisting the parents to take care of their babies using Artificial Intelligence (AI). There has implemented many algorithms to classify and provide assistance but many of them didn’t predicted the correct meaning more accurately. So, here, in this implementation, we’re using the deep learning classifier namely Artificial Neural Networks (ANN) which has many applications, one among them are as a classifier which we’re using in this implementation. We can use our defined dataset for training and testing of ANN classifier using spectrogram features extracted using Short Time Fourier Transform (STFT) and apply PCA to reduce redundant data from the features and to improve the performance of the classification results will classify whether the infant cry is because of hungry or pain or anger and finally performance of classifier is validated using accuracy.
Keywords: Infant Cry Classification, Artificial Intelligence (AI), Short Time Fourier Transform (STFT), Artificial Neural Networks (ANN), Accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Demo Video
Request for Video
Paper PublishingRequest Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
