Improved edit detection in speech via ENF patterns

In a recent paper published in the IEEE TIFS, we proposed an edit detection method based on the instantaneous variations of the Electrical Network Frequency (ENF). In this work we modify the detection criteria of that method by taking advantage of the typical pattern of ENF variations elicited by audio edits. We describe the implemented modifications and directly confront the performance of both methods using two distinct signal databases that contain real-life speech recordings. The experimental results demonstrate that the new proposition has an improved performance in terms of lower equal error rates when compared to its former version.