Acoustic Scene Classification using Deep Learning Architectures

Spoorthy. V. Spoorthy., Manjunath Mulimani, Shashidhar G. Koolagudi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2021 6th International Conference for Convergence in Technology, I2CT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728188768
DOIs
Publication statusPublished - 02-04-2021
Event6th International Conference for Convergence in Technology, I2CT 2021 - Pune, India
Duration: 02-04-202104-04-2021

Publication series

Name2021 6th International Conference for Convergence in Technology, I2CT 2021

Conference

Conference6th International Conference for Convergence in Technology, I2CT 2021
Country/TerritoryIndia
CityPune
Period02-04-2104-04-21

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Artificial Intelligence
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

Fingerprint

Dive into the research topics of 'Acoustic Scene Classification using Deep Learning Architectures'. Together they form a unique fingerprint.

Cite this