ArticlePloS one2025
A novel approach to porcine abnormal sounds recognition based on improved Multi-SVDD.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the real-time recognition of porcine abnormal sounds, the accuracy and stability of the recognition method are crucial to guarantee a good performance. For this purpose, an improved Multiple-Support Vector Data Description (Multi-SVDD) is proposed in this paper. Firstly, the improved spectral subtraction using improved Minima Controlled Recursive Averaging (IMCRA) and Spectral Subtraction (SS) is applied to remove the noise of collected sounds. Then, the Mel-Frequency Cepstral Coefficients (MFCC) and first-order differential MFCC (ΔMFCC) are extracted as feature parameters. Finally, the Multi-SVDD is used to detect and recognize the porcine abnormal sounds. In order to improve the accuracy and error-tolerance of Multi-SVDD for human errors on tagging data, the space density information of training data is calculated as the confidences to reduce the interference of outliers in the process of Multi-SVDD training. The experimental results show that the accuracy, precision and recall of the proposed method are as high as 95.0%, 95.4% and 95.0% respectively, which indicates higher error-tolerance capability than classical SVDD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.