Acoustic Scene Classification using Deep Learning Architectures
| Autor: | Shashidhar G. Koolagudi, Spoorthy., Manjunath Mulimani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Task (project management) Convolution Recurrent neural network Computational auditory scene analysis 0202 electrical engineering electronic engineering information engineering Baseline system 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2021 6th International Conference for Convergence in Technology (I2CT). |
| DOI: | 10.1109/i2ct51068.2021.9418177 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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