Using Approximated Auditory Roughness as a Pre-filtering Feature for Human Screaming and Affective Speech AED

Detecting human screaming, shouting, and other verbal manifestations of fear and anger are of great interest to security Audio Event Detection (AED) systems. The Internet of Things (IoT) approach allows wide-covering, powerful AED systems to be distributed across the Internet. But a good feature to prefilter the audio is critical to these systems. This work evaluates the potential of detecting screaming and affective speech using Auditory Roughness and proposes a very light-weight approximation method. Our approximation uses a similar amount of Multiple Add Accumulate (MAA) compared to short-term energy (STE), and at least 10x less MAA than MFCC. We evaluated the performance of our approximated roughness on the Mandarin Affective Speech corpus and a subset of the Youtube AudioSet for screaming against other low-complexity features. We show that our approximated roughness returns higher accuracy.