Abstract
Audio fingerprinting involves extraction of quantitative frequency descriptors that can be used for indexing, search and retrieval of audio signals in sound recognition software. We propose a similar approach with medical ultrasonographic Doppler audio signals. Power Doppler periodograms were generated from 84 ultrasonographic Doppler signals from the common carotid arteries in 22 dogs. Frequency features were extracted from each periodogram and included in a principal component analysis (PCA). From this 10 audio samples were pairwise classified as being either similar or dissimilar. These pairings were compared to a similar classification based on standard quantitative parameters used in medical ultrasound and to classification performed by a panel of listeners. The ranking of sound files according to degree of similarity differed between the frequency and conventional classification methods. The panel of listeners had an 88% agreement with the classification based on quantitative frequency features. These findings were significantly different from the score expected by chance (p < 0.001). The results indicate that the proposed frequency based classification has a perceptual relevance for human listeners and that the method is feasible. Audio fingerprinting of medical Doppler signals is potentially useful for indexing and search for similar and dissimilar audio samples in a dataset.
Highlights
Ranking Pairing dissimilarities among the extracted features are identified using principal component analysis (PCA)
A PCA including the time domain Doppler values for each dog showed no evident grouping of the scores, i.e. there was a clear overlap between samples from dogs of different sex, age and size, as well as between the left and right sides of the neck and transducer direction
We present a novel approach to analysis of arterial Doppler signals
Summary
Ranking Pairing dissimilarities among the extracted features are identified using principal component analysis (PCA). The validity of the results is tested against similar classifications made by subjective human evaluation. The novelty of the analysis is tested by a comparison with a classification based on conventional medical Doppler parameters. We propose that audio signal fingerprinting has a role in medical ultrasound, as it provides the potential to allow efficient indexing and search, and can contribute to patient care
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