?EARS is the concept of implementation of live environmental audio processing & recognition . Our Project will detect different environmental sounds which we encounter in daily life like glass breaks, human screams, gunshots, explosions or door slams, honks, and other sou
EARS or Environmental Audio Recognition System
•EARS is the concept of implementation of live environmental audio processing & recognition . Our Project will detect different environmental sounds which we encounter in daily life like glass breaks, human screams, gunshots, explosions or door slams, honks, and other sounds.
The goal of this project is the characterization of environmental sounds for understanding and to make these sound valuable to us. Our Project will detect different enviornmentel sounds which we encounter in daily life like glass breaks, human screams, gunshots, explosions or door slams, honks, and other sounds. we classify them in diffent classes . we shall use python, deep learning, CNN, and different data sets like MNIST.
used Python language.
BENEFITS : it has various applications. Robotics: Robot can perform specific task by detecting a specific sound.
Vehical Industry :
Vehicals can be automated by detection of sound e.g they can turn on and off music system by detection of mobile tone . glass of car window can be controlled when it detects noise outside .
Home Automation :
It plays an important role we can detect sounds like human screams, gunshots, explosions , door bells, child crying ,falling of object ,car starting, loud Volume of TV and ringing telephone etc . Recording Devices:
Devices which can operate in noisy enviornment ,to supress noise and focus on sound which is to be recorded. etc.
•OUR PROJECT IS USED FOR AUTOMATIC DETECTION AND RECOGNITION OF DIFFERENT ENVIRONMENTAL SOUNDS , SUCH AS GLASS BREAK, human screams, gunshots, explosions or door slams, honks, and other sounds.
•A COMPLETE DETECTION AND IDENTIFACATION WILL BE DISCRIBED.
• sound database containing more than 800 signals WILL BE distributed among FEW different classes.
•The detection algorithm, based on a median filter, features a highly robust performance even under important background noise conditions.
•THIS PROJECT WILL BE ABLE TO SUPPRESSED UNWANTED BACKGROUND SONDS FROM LIVE RECORDING.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| respberry pi3 B+ | Equipment | 1 | 6500 | 6500 |
| data cable | Equipment | 2 | 250 | 500 |
| micro SD Card | Equipment | 1 | 1500 | 1500 |
| mic | Equipment | 2 | 1300 | 2600 |
| hdmi cable | Miscellaneous | 1 | 250 | 250 |
| vga converter | Miscellaneous | 1 | 500 | 500 |
| lcd display | Equipment | 1 | 5000 | 5000 |
| mouse | Equipment | 1 | 800 | 800 |
| keyboard | Equipment | 1 | 500 | 500 |
| triport stand | Miscellaneous | 1 | 1000 | 1000 |
| Total in (Rs) | 19150 |
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