TY - GEN
T1 - Acoustic Scene Classification using Deep Learning Architectures
AU - V. Spoorthy., Spoorthy.
AU - Mulimani, Manjunath
AU - Koolagudi, Shashidhar G.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/2
Y1 - 2021/4/2
N2 - Enabling devices to make sense of sound is known as Acoustic Scene Classification (ASC). The analysis of various scenes by applying computational algorithms is known as computational auditory scene analysis. The main aim of this paper is to classify audio recordings based on the scenes/environment in which they are recorded. Deep learning is amongst the recent trends in most of the applications. In this paper, two deep learning algorithms are used to perform the classification of acoustic scenes, namely Convolution Neural Network (CNN) and Convolution-Recurrent Neural Network (CRNN). The model is evaluated on three activation functions, namely, ReLU, LeakyReLU and ELU. The highest recognition accuracy achieved for ASC task is 90.96% from CRNN model. The model performed well on basic convolution architecture with 10.9% improvement from the baseline system of this task.
AB - Enabling devices to make sense of sound is known as Acoustic Scene Classification (ASC). The analysis of various scenes by applying computational algorithms is known as computational auditory scene analysis. The main aim of this paper is to classify audio recordings based on the scenes/environment in which they are recorded. Deep learning is amongst the recent trends in most of the applications. In this paper, two deep learning algorithms are used to perform the classification of acoustic scenes, namely Convolution Neural Network (CNN) and Convolution-Recurrent Neural Network (CRNN). The model is evaluated on three activation functions, namely, ReLU, LeakyReLU and ELU. The highest recognition accuracy achieved for ASC task is 90.96% from CRNN model. The model performed well on basic convolution architecture with 10.9% improvement from the baseline system of this task.
UR - http://www.scopus.com/inward/record.url?scp=85106474124&partnerID=8YFLogxK
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U2 - 10.1109/I2CT51068.2021.9418177
DO - 10.1109/I2CT51068.2021.9418177
M3 - Conference contribution
AN - SCOPUS:85106474124
T3 - 2021 6th International Conference for Convergence in Technology, I2CT 2021
BT - 2021 6th International Conference for Convergence in Technology, I2CT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference for Convergence in Technology, I2CT 2021
Y2 - 2 April 2021 through 4 April 2021
ER -