Abstract
Monitoring of daily activities is highly important to build environmental intelligence. Especially monitoring of house appliances is a key point for creating an intelligent home environment. Run levels of home appliances can be useful to detect such activities. Many house appliances produce different sounds during their differerent run levels. In this paper, we focus on recognition of running house appliances based on sound samples collected from house environment. MFCC and physical features of the sound are tested. Performance of both k-NN and SVM are evaluated. Our proposed system is able to identify working house appliances with 98% success rate.
Keywords
Mel-frequency cepstrum
Sound (geography)
Computer science
Focus (optics)
Key (lock)
Home automation
Ambient intelligence
Support vector machine
Feature extraction
Artificial intelligence
Telecommunications
Computer security
Acoustics
Subject Areas
Music and Audio Processing
·Signal Processing ·Physical Sciences
Speech and Audio Processing
·Signal Processing ·Physical Sciences
Noise Effects and Management
·Speech and Hearing ·Health Sciences
Citations by Year
2012: 1
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Life in Land 49%
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