Determining Guava Freshness by Flicking Signal Recognition Using HMM Acoustic Models
Determining Guava Freshness by Flicking Signal Recognition Using HMM Acoustic Models
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DOI：
10.7763/ijcte.2013.v5.815
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
R. Phoophuangpairoj
中科院分区：
文献类型：
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作者：
R. Phoophuangpairoj
能够自动确定水果的新鲜度或质量非常重要，因为世界上的人们都在消费水果。无数的水果买家在购买不新鲜、陈旧或不合格的农产品时可能会感到失望。研究和开发一种计算机化的方法，帮助确定水果的新鲜度，而不切割，破坏或品尝是有趣的，因为它可以造福于世界各地的人们。提出了一种基于非闪烁约简预处理和不同新鲜度的声学模型的方法，用于识别新鲜和不新鲜的番石榴闪烁信号。在识别过程中，首先，信号的非闪烁部分被减少。然后，提取信号的频谱特征。最后，1）使用隐马尔可夫模型（HMM）创建声学模型，2）定义新鲜和不新鲜番石榴的声学序列，以及3）应用定义的可能的新鲜度识别结果来确定番石榴新鲜度。所提出的方法导致新鲜，3和6天保持番石榴未知测试集的平均正确新鲜度识别率分别为92.00%，88.00%和94.00%。当使用1到5个闪烁时，平均正确识别率分别为90.00%、90.67%、92.00%、92.00%和92.00%。当使用从一到五的任何数量的轻弹时，平均识别时间小于50毫秒。结果表明，所提出的方法，使用三到五个轻弹是时间效率和准确度足以用于确定番石榴的质量。
Being able to determine the freshness or quality of fruit automatically is significant because people in the world consume fruit. Countless fruit buyers can be disappointed when they purchase stale, old or sub-standard produce. Studying and developing a computerized method that helps to determine the freshness of fruit without cutting, destroying or tasting is interesting because it could be of benefit to people worldwide. A method using non-flicking reduction preprocessing and acoustic models of different freshness levels is proposed to recognize fresh and not fresh guava flicking signals. In the recognition process, first, the non-flicking parts of the signals are reduced. Then, spectral features of the signals are extracted. Finally, 1) acoustic models are created using Hidden Markov Models (HMM), 2) acoustic sequences of fresh and not fresh guavas are defined and 3) defined possible freshness recognition results are applied to determine guava freshness. The proposed method resulted in average correct freshness recognition rates of 92.00%, 88.00% and 94.00% from fresh, 3 and 6-day-kept guava unknown test sets, respectively. Average correct freshness recognition rates of 90.00%, 90.67%, 92.00%, 92.00% and 92.00% were obtained when using one through five flicks, respectively. An average recognition time of less than 50 milliseconds was taken when using any number of flicks from one to five. The results indicate that the proposed method using three to five flicks is time-efficient and accurate enough to be used to determine the quality of guavas.