Acoustic Detection of Human Activities in Natural Environments
Automatic recognition of sound events can be valuable for efficient analysis of audio scenes. For example, detecting human activities like trespassing and hunting in natural environments can play an important role in their preservation by alerting authorities to take action. In the proposed system, each sound class is represented by a hidden Markov model created from descriptors in the time, frequency, and wavelet domains. The system has the ability to automatically adapt to acoustic conditions of different scenes via the feedback loop that refines an unsupervised model. A reliable testing process was adopted for assessing the performance of the system under adverse conditions characterized by highly nonstationary environmental noise.
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