Acoustic feedback is a recurrent problem in live sound reinforcement scenarios. Many attempts have been made to produce an automated feedback cancellation system, but none have seen widespread use due to concerns over the accuracy and transparency of feedback howl cancellation. This paper investigates the use of the Magnitude Slope Deviation (MSD) algorithm to intelligently identify feedback howl in live sound scenarios. A new variation on this algorithm is developed, tested, and shown to be much more computationally efficient without compromising detection accuracy. The effect of varying the length of the frequency spectrum history buffer available for analysis is evaluated across various live sound scenarios. The MSD algorithm is shown to be very accurate in detecting howl frequencies amongst the speech and classical music stimuli tested here, but inaccurate in the rock music scenario even when a long history buffer is used. Finally, a new algorithm for setting the depth of howl-cancelling notch filters is proposed and investigated. The algorithm shows promise in keeping frequency attenuation to a minimum required level, but the approach has some problems in terms of time taken to cancel howl.

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