In this paper, we propose a method for characterizing a musical signal by extracting a set of harmonic descriptors reflecting the maximum information contained in this signal. We focus our study on a signal of orienta...In this paper, we propose a method for characterizing a musical signal by extracting a set of harmonic descriptors reflecting the maximum information contained in this signal. We focus our study on a signal of oriental music characterized by its richness in tone that can be extended to 1/4 tone, taking into account the frequency and time characteristics of this type of music. To do so, the original signal is slotted and analyzed on a window of short duration. This signal is viewed as the result of a combined modulation of amplitude and frequency. For this result, we apply short-term the non-stationary sinusoidal modeling technique. In each segment, the signal is represented by a set of sinusoids characterized by their intrinsic parameters: amplitudes, frequencies and phases. The modeling approach adopted is closely related to the slot window;therefore great importance is devoted to the study and the choice of the kind of the window and its width. It must be of variable length in order to get better results in the practical implementation of our method. For this purpose, evaluation tests were carried out by synthesizing the signal from the estimated parameters. Interesting results have been identified concerning the comparison of the synthesized signal with the original signal.展开更多
针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法...针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。展开更多
文摘In this paper, we propose a method for characterizing a musical signal by extracting a set of harmonic descriptors reflecting the maximum information contained in this signal. We focus our study on a signal of oriental music characterized by its richness in tone that can be extended to 1/4 tone, taking into account the frequency and time characteristics of this type of music. To do so, the original signal is slotted and analyzed on a window of short duration. This signal is viewed as the result of a combined modulation of amplitude and frequency. For this result, we apply short-term the non-stationary sinusoidal modeling technique. In each segment, the signal is represented by a set of sinusoids characterized by their intrinsic parameters: amplitudes, frequencies and phases. The modeling approach adopted is closely related to the slot window;therefore great importance is devoted to the study and the choice of the kind of the window and its width. It must be of variable length in order to get better results in the practical implementation of our method. For this purpose, evaluation tests were carried out by synthesizing the signal from the estimated parameters. Interesting results have been identified concerning the comparison of the synthesized signal with the original signal.
文摘针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。
