Learn More
This paper presents a new method using principal component analysis (PCA) to eliminate data redundancy in loudspeaker fault detection. It uses wavelet packet transformation (WPT) to decompose the loudspeaker acoustics signal into 32 packet node signals. Then, get the mean, max, standard deviation and correlation coefficient of every node envelopment .With(More)
The analysis of loudspeaker response signal is one of the main methods of loudspeaker fault diagnosis. In this paper, the loudspeaker response signal is treated by the pre emphasis in time domain, and it is processed by short time Fourier transform. More evident time-frequency feature of fault loudspeaker is obtained than before pretreatment. It provides a(More)
In order to solve the problem of loudspeaker fault detection, the fault characteristic region of different fault types of louderspeaker can be identified based on the time-frequency analysis of the louderspeaker respond signals (LRS). The louderspeaker fault characteristic vector can be built. And the fault louderspeakers can be identified and classified by(More)
In this paper, the short-time Fourier transformation (STFT) is adopted to transform the loudspeaker sound signal. By STFT, the one-dimensional loudspeaker response signal is converted into two-dimensional time-frequency figure. Then, the figure is decomposed into a number of areas according to its harmonics distribution. The peak and mean values of every(More)
  • 1