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Updated: Apr 30, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Multi-scale pyramid pooling with low complexity for acoustic scene classification.

Pengxu Jiang¹, Xusheng Liu², Peng Li²

¹Kaifeng Central Hospital, Kaifeng, 475000, China. pxjiangxx@163.com.

Scientific Reports

|April 28, 2026

Summary

This summary is machine-generated.

This study introduces a low-complexity multi-scale pyramid pooling (MSP) strategy to improve acoustic scene classification (ASC) performance on devices with limited parameters. The novel approach enhances baseline convolutional neural networks (CNNs) without significant computational overhead.

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Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Acoustic scene classification (ASC) identifies acoustic environments using convolutional neural networks (CNNs).
High-performance CNNs require many parameters, posing challenges for deployment on resource-constrained devices.
Existing ASC systems struggle with limited parameter budgets.

Purpose of the Study:

To enhance the performance of baseline CNNs for ASC under low-parameter constraints.
To introduce a low-complexity multi-scale pyramid pooling (MSP) strategy for CNNs.
To improve the efficacy of ASC systems on lightweight devices.

Main Methods:

Implemented a multi-scale pyramid pooling (MSP) strategy across convolutional layers of varying depths.
MSP captures correlation information among local feature maps with diverse time-frequency details.
Analyzed the contribution of sound events to scenes by processing feature maps.

Main Results:

MSP modules significantly improved baseline CNN performance on ASC tasks.
Achieved performance gains of 5.26% (DCASE 2019) and 4.38% (DCASE 2020) with only 4.99k additional parameters.
Demonstrated effectiveness in resource-constrained ASC systems.

Conclusions:

The proposed MSP module effectively enhances performance for parameter-constrained ASC systems.
MSP offers a viable solution for deploying advanced ASC on edge devices.
Potential applications include intelligent surveillance, smart wearables, and edge audio monitoring.