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Audio noise detection using hidden Markov model

机译:使用隐马尔可夫模型进行音频噪声检测

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Simple noise level monitoring systems, which are currently used to create noise map in residential areas, are unable to identify source of environmental noise. The proposed automatic noise recognition (ANR) system can be used in conjunction with simple noise level monitoring to create an intelligent noise monitoring system (INMS). The presented system which is focused on aircraft noise detection, consists of two parts: feature extractor and training-recognition. We append linear prediction coefficients to Cepstrum coefficients to make a rich feature extractor. The hidden Markov model (HMM) is used for training and recognition. The required observation sequence is obtained by means of a vector quantization method based on fuzzy C-mean clustering. 15 signals are used for training and 28 signals are used in test phase. An overall 83% accuracy in classification is achieved.
机译:当前用于在居民区中创建噪声图的简单噪声级别监视系统无法识别环境噪声的来源。提议的自动噪声识别(ANR)系统可与简单的噪声级别监视结合使用,以创建智能噪声监视系统(INMS)。提出的针对飞机噪声检测的系统包括两部分:特征提取器和训练识别。我们将线性预测系数附加到倒谱系数,以构成丰富的特征提取器。隐藏的马尔可夫模型(HMM)用于训练和识别。所需的观察序列是通过基于模糊C均值聚类的矢量量化方法获得的。测试阶段使用15个信号,测试阶段使用28个信号。总体上实现了83%的分类准确率。

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