In this article, an evaluation of a recently published hum detection algorithm for audio signals is presented. To determine the performance of the method, large amounts of artificially generated and real-world audio data, containing a variety of music and speech recordings, are processed by the algorithm. By comparing the detection results with manually determined ground truth data, several error measures are computed: hit and false alarm rates, frequency deviation of the hum frequency estimation, offset of detected start and stop times and the accuracy of the SNR estimation.
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