With the increased proliferation of interconnected devices that have built-in microphones, acoustic event classification and monitoring becomes possible in a wide variety of applications, such as surveillance, healthcare, military, machine diagnostics, and wildlife tracking. The promise and success of these applications depends on robust sensing of acoustic events in the environment. Typically, sound event classes are defined by annotating training data, which is a laborious process. This work introduces an extended version of non-negative matrix deconvolution (NMD), called low-resolution multi-label non-negative matrix deconvolution (LRM-NMD), where both the observation data and the available labeling information are used during training. The proposed extension of NMD was successfully applied to the classification of acoustic events even in noisy conditions with overlapping events. Low-resolution, multi-labeling information simply indicates that the sound classes of the events take place over a longer period of time in the acoustic data without identifying beginning or endings of the individual events.
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