M. Brandt, and J. Bitzer, "Automatic Detection of Hum in Audio Signals," J. Audio Eng. Soc., vol. 62, no. 9, pp. 584-595, (2014 September.). doi: https://doi.org/10.17743/jaes.2014.0034
M. Brandt, and J. Bitzer, "Automatic Detection of Hum in Audio Signals," J. Audio Eng. Soc., vol. 62 Issue 9 pp. 584-595, (2014 September.). doi: https://doi.org/10.17743/jaes.2014.0034
Abstract: Hum (power line interference in the form of stationary sine waves and their harmonics) is often introduced into an audio signal during the recording or copying process. These disturbances can distract the listener, affect dynamics processing devices or even overload loudspeakers. The first stage in a removal process requires an accurate detection of the existence of hum and its parameters. This investigation presents an automatic method for detecting hum components with a low false alarm rate. The foundation of the detection algorithm is a statistical analysis of the short-term Fourier transform of the input signal. The algorithm was tested with both artificial signals and real recordings.
@article{brandt2014automatic,
author={brandt, matthias and bitzer, joerg},
journal={journal of the audio engineering society},
title={automatic detection of hum in audio signals},
year={2014},
volume={62},
number={9},
pages={584-595},
doi={https://doi.org/10.17743/jaes.2014.0034},
month={september},}
@article{brandt2014automatic,
author={brandt, matthias and bitzer, joerg},
journal={journal of the audio engineering society},
title={automatic detection of hum in audio signals},
year={2014},
volume={62},
number={9},
pages={584-595},
doi={https://doi.org/10.17743/jaes.2014.0034},
month={september},
abstract={hum (power line interference in the form of stationary sine waves and their harmonics) is often introduced into an audio signal during the recording or copying process. these disturbances can distract the listener, affect dynamics processing devices or even overload loudspeakers. the first stage in a removal process requires an accurate detection of the existence of hum and its parameters. this investigation presents an automatic method for detecting hum components with a low false alarm rate. the foundation of the detection algorithm is a statistical analysis of the short-term fourier transform of the input signal. the algorithm was tested with both artificial signals and real recordings.},}
TY - paper
TI - Automatic Detection of Hum in Audio Signals
SP - 584
EP - 595
AU - Brandt, Matthias
AU - Bitzer, Joerg
PY - 2014
JO - Journal of the Audio Engineering Society
IS - 9
VO - 62
VL - 62
Y1 - September 2014
TY - paper
TI - Automatic Detection of Hum in Audio Signals
SP - 584
EP - 595
AU - Brandt, Matthias
AU - Bitzer, Joerg
PY - 2014
JO - Journal of the Audio Engineering Society
IS - 9
VO - 62
VL - 62
Y1 - September 2014
AB - Hum (power line interference in the form of stationary sine waves and their harmonics) is often introduced into an audio signal during the recording or copying process. These disturbances can distract the listener, affect dynamics processing devices or even overload loudspeakers. The first stage in a removal process requires an accurate detection of the existence of hum and its parameters. This investigation presents an automatic method for detecting hum components with a low false alarm rate. The foundation of the detection algorithm is a statistical analysis of the short-term Fourier transform of the input signal. The algorithm was tested with both artificial signals and real recordings.
Hum (power line interference in the form of stationary sine waves and their harmonics) is often introduced into an audio signal during the recording or copying process. These disturbances can distract the listener, affect dynamics processing devices or even overload loudspeakers. The first stage in a removal process requires an accurate detection of the existence of hum and its parameters. This investigation presents an automatic method for detecting hum components with a low false alarm rate. The foundation of the detection algorithm is a statistical analysis of the short-term Fourier transform of the input signal. The algorithm was tested with both artificial signals and real recordings.
