Browsing by Subject "Audio processing"

Audio signals offer invaluable insights into system operational conditions and potential malfunctions. Proactive fault detection in machinery and other infrastructures through audio monitoring provides significant advantages in numerous sectors, such as industrial maintenance, healthcare, and urban security. Localizing anomalies within the spectral content of audio data opens possibilities to not only diagnose but also effectively address the underlying issues. This thesis addresses the challenge of comprehensively capturing the full context of anomalies detected within audio data. To achieve this, we have developed a novel unsupervised method that adapts visual anomaly localization techniques specifically for the analysis of audio data. This approach utilizes visual representations of audio signals, particularly spectrograms, to apply the Student-Teacher Feature Pyramid Matching Method (STFPM) within an unsupervised learning framework. By harnessing the inherent visual patterns in audio data, our method enables precise localization of anomalies. By augmenting the MIMII dataset with synthetic anomalies and conducting extensive testing, we validated our approach’s ability to localize anomalies in audio data. The findings confirm that our model not only detects but also precisely pinpoints the location of these artificially introduced anomalies within audio spectrograms in terms of both time and frequency. This demonstrates the precision and reliability of our approach, highlighting its potential as a promising solution for accurately localizing anomalies in various audio applications.