Separation of Multiple Stationary Sound Sources using convolutional neural network

This paper presents a deep neural network-based approach for separating multiple sound sources located at different positions in-room environments. The Mixed signal is recorded using a Linear microphone array in-room environment. We have also shown that the known signals are correctly separated with...

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Bibliographic Details
Published in2021 6th International Conference for Convergence in Technology (I2CT) pp. 1 - 6
Main Authors Mali, Swapnil G., Dhale, Mohit V., Mahajan, Shriniwas P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.04.2021
Subjects
convolutional neural network
deep learning
Gaussian noise
Microphone arrays
Particle separators
Room Impulse Response
short-time Fourier transform
Source separation
System performance
Training
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Summary:This paper presents a deep neural network-based approach for separating multiple sound sources located at different positions in-room environments. The Mixed signal is recorded using a Linear microphone array in-room environment. We have also shown that the known signals are correctly separated with the Convolutional Neural Network-based algorithm using mapping mixed-signals with the original signal. We have proposed a novel separation architecture and database creation algorithm with various combinations of gaussian noise source signals to create a diverse mixture for training and testing purposes. After the sound separation model's training and testing, it is shown that the system's performance is better compared with other sound separation models. It is beneficial to separate the multiple mixed signals in a known source case compared to an unknown source case.
DOI:10.1109/I2CT51068.2021.9417983