The research conducted within the audio signal processing field is increasingly focusing on environmental sound classification. This paper presents a low-complexity Fully Convolutional Network composed of two parallel branches. These branches are responsible for extracting features from the Cadence Frequency Diagram representation and the Chebychev moments, respectively. By utilizing both domains of machine-and deep-learning, the proposed pipeline takes advantage of the unique characteristics of each. The key strength of this architecture lies in its reduced number of layers and parameters, as well as its ability to efficiently compute the Cadence Frequency Diagram and Chebychev moments. The effectiveness of the proposed pipeline is demonstrated through various tests conducted on two audio datasets, namely UrbanSound8K and ESC-50.

Low-Complexity Environmental Sound Classification using Cadence Frequency Diagram and Chebychev Moments

Pallotta, Luca;
2023-01-01

Abstract

The research conducted within the audio signal processing field is increasingly focusing on environmental sound classification. This paper presents a low-complexity Fully Convolutional Network composed of two parallel branches. These branches are responsible for extracting features from the Cadence Frequency Diagram representation and the Chebychev moments, respectively. By utilizing both domains of machine-and deep-learning, the proposed pipeline takes advantage of the unique characteristics of each. The key strength of this architecture lies in its reduced number of layers and parameters, as well as its ability to efficiently compute the Cadence Frequency Diagram and Chebychev moments. The effectiveness of the proposed pipeline is demonstrated through various tests conducted on two audio datasets, namely UrbanSound8K and ESC-50.
2023
979-8-3503-1536-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/172875
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