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Updated: Jul 20, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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通过深度神经网络对食品材料的脆度进行分类
Rafael Z Lopes1, Gustavo C Dacanal1
1Department of Food Engineering, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, Brazil.
Journal of texture studies
|August 1, 2023
概括
这项研究使用机器学习来分析脆脆的食物声音,在分类像条和烤面包这样的食物方面达到95%以上的准确性. 它强调了ASMR音频在训练人工智能模型以了解食物质感方面的潜力.
科学领域:
- 食品科学 食品科学 食品科学
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 脆度是影响消费者偏好和产品开发的关键食物质感.
- 之前的研究使用了机械粉碎和神经网络来分析脆脆的声音.
- 了解清晰度需要在时间和频率领域分析声音.
研究的目的:
- 调查时间和频率域中脆度的表示.
- 确定关键的声学参数,以区分不同的脆口食品.
- 通过音频数据评估机器学习模型来对清脆度进行分类.
主要方法:
- 使用多层感知器 (MLP) 分析Mel频率塞普斯特拉系数 (MFCC).
- 使用残余神经网络 (ResNet) 分析离散里埃转换 (DFT) 数据.
- 利用ASMR视频中的音频样本进行模型培训和验证.
主要成果:
- 两种MLP (MFCC) 和ResNet (DFT) 模型都实现了超过95%的样本准确度,用于分类炸,片和烤面包.
- 该MLP模型显示出更大的稳定性,成功预测了外部音频输入.
- ResNet模型对DFT频谱变化更敏感,但对外部数据效果较差.
结论:
- 机器学习模型可以有效地使用声学特征来分类食物的脆度.
- ASMR音频提供了一个可行的,大规模的数据集,用于训练人工智能模型对食品质感的训练.
- 模型的选择 (MLP与ResNet) 取决于具体的应用,无论是对已知的样本进行分类还是对新样本进行概括.
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