Abstract Contiguous short-term spectral estimates of non-stationary acoustic signals are often displayed as intensity-modulated frequency-time pictures (‘spectrograms’). Some of the problems and advantages in storing and processing certain types of spectrograms as digital pictures are explored. The particular type of spectrogram discussed is from a source producing a time-variant ‘line’ spectrum consisting of one dominant primary line and several much weaker harmonic or quasi-harmonic lines. This type of spectrogram is commonly produced by rotating machinery. It is supposed that this signal is measured amidst additive broad band noise. The resulting spectrogram consists of a family of related meandering lines, the weaker members of which tend to be lost in the noise. In the proposed picture analysis scheme the raw spectral estimates are stored as a digital spectrogram. This spectrogram is then processed by line enhancement methods to extract the primary line to form a ‘family-template’. Using this template the weaker lines can be detected and extracted. By re-assembling the extracted components, a cleaned-up low-entropy frequency-time picture of the signal is produced which shows more structure and which is much more amenable to automatic analysis than the original spectrogram.
[1]
David J. Parker,et al.
Analysis of global pattern features
,
1974,
Pattern Recognit..
[2]
Spectral Measurements of Sliding Tones
,
1960
.
[3]
Ugo Montanari,et al.
On the optimal detection of curves in noisy pictures
,
1971,
CACM.
[4]
D. G. Nichol,et al.
The processing of bathythermograph data: A picture analysis approach
,
1976,
Pattern Recognit..
[5]
John Bowman Thomas,et al.
An introduction to statistical communication theory
,
1969
.
[6]
William S. Meisel,et al.
Computer-oriented approaches to pattern recognition
,
1972
.
[7]
Douglas J. H. Moore,et al.
An Approach to the Analysis and Extraction of Pattern Features Using Integral Geometry
,
1972,
IEEE Trans. Syst. Man Cybern..
[8]
H. C. Andrews.
Automatic interpretation and classification of images by use of the Fourier domain
,
1969
.
[9]
A. Rosenfeld,et al.
Edge and Curve Detection for Visual Scene Analysis
,
1971,
IEEE Transactions on Computers.