一个轻量级的双分支掩盖网络用于环境声音分类
Guorong Chen1,2, Bao Zhang3, Zhikang Ding1
1School of Computer Science and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Scientific reports
|December 31, 2025
概括
SpectroMaskNet使用紧的双分支模型提供高效的环境声音分类 (ESC). 它在基准数据集上实现了高精度,在没有广泛的预训练的情况下超过了轻量级方法,非常适合资源有限的应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 深度神经网络 (DNN) 具有先进的环境声音分类 (ESC).
- 对于ESC而言,现有的DNN通常需要大型模型和广泛的预训练,限制在资源有限的环境中部署.
- 轻量级模型可能具有有限的表示能力和低于最佳的概括,特别是在低数据场景中.
研究的目的:
- 提出SpectroMaskNet,一个紧的双分支架构,以实现高效和强大的ESC.
- 解决现有的轻量级模型在表示能力和概括性方面存在的局限性.
- 在资源有限的环境和数据稀缺的情况下,使有效的社会经济委员会成为可能.
主要方法:
- 开发了SpectroMaskNet,一个紧的双分支神经网络架构.
- 集成的全球-本地注意力机制,以捕捉长期的时间依赖和细粒度的光谱特征.
- 采用区块掩盖谱图增强来增强模型的稳定性和概括性,特别是在低数据场景中.
主要成果:
- 在基准数据集上,SpectroMaskNet实现了高准确度:ESC-10 (97.50%),ESC-50 (95.50%),UrbanSound8K (96.32%) 和SpeechCommandV2 (96.52%).
- 该模型在不需要大规模预训练的情况下超过了现有的轻量级基线.
- SpectroMaskNet展示了较低的计算复杂性,适用于高效和可扩展的现实世界ESC应用程序.
结论:
- SpectroMaskNet为高效准确的环境声音分类提供了一个有前途的解决方案.
- 拟议的架构有效地平衡了模型紧性与高性能和通用化能力.
- 该模型的效率和可扩展性使其适合在各种ESC应用中实际部署.
更多相关视频
10:13A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
Published on: November 26, 2012
14.7K
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
396
相关概念视频
Masking and Demasking Agents
3.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.4K
Classification of Signals
1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Force Classification
2.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.2K
Difference from Background: Limit of Detection
8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
8.0K
Classification of Systems-I
533
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
533
