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Microphone Handling Noise: Measurements of Perceptual Threshold and Effects on Audio Quality.

Kendrick P, Jackson IR, Fazenda BM, Cox TJ, Li FF - PLoS ONE (2015)

Bottom Line: Other factors such as noise type or background noise in the listening environment did not significantly affect quality ratings.Podcast, microphone type and reproduction equipment were found to be significant but only to a small extent.The SNR threshold at which 50% of subjects noticed handling noise was found to be 4.2 ± 0.6 dBA.

View Article: PubMed Central - PubMed

Affiliation: Acoustics Research Centre, School of Computing Science and Engineering, University of Salford, Salford, M5 4WT, United Kingdom.

ABSTRACT
A psychoacoustic experiment was carried out to test the effects of microphone handling noise on perceived audio quality. Handling noise is a problem affecting both amateurs using their smartphones and cameras, as well as professionals using separate microphones and digital recorders. The noises used for the tests were measured from a variety of devices, including smartphones, laptops and handheld microphones. The signal features that characterise these noises are analysed and presented. The sounds include various types of transient, impact noises created by tapping or knocking devices, as well as more sustained sounds caused by rubbing. During the perceptual tests, listeners auditioned speech podcasts and were asked to rate the degradation of any unwanted sounds they heard. A representative design test methodology was developed that tried to encourage everyday rather than analytical listening. Signal-to-noise ratio (SNR) of the handling noise events was shown to be the best predictor of quality degradation. Other factors such as noise type or background noise in the listening environment did not significantly affect quality ratings. Podcast, microphone type and reproduction equipment were found to be significant but only to a small extent. A model allowing the prediction of degradation from the SNR is presented. The SNR threshold at which 50% of subjects noticed handling noise was found to be 4.2 ± 0.6 dBA. The results from this work are important for the understanding of our perception of impact sound and resonant noises in recordings, and will inform the future development of an automated predictor of quality for handling noise.

No MeSH data available.


Example spectrograms of rubbing type microphone handling noises.Recorded on the a) SM58, the b) iPhone and the c) AT803b.
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pone.0140256.g006: Example spectrograms of rubbing type microphone handling noises.Recorded on the a) SM58, the b) iPhone and the c) AT803b.

Mentions: Figs 1–3 display the waveforms of six of the handling noise examples for the times selected by the automatic segmentation algorithm. Each waveform is normalised to maximum absolute level and one impact and one rubbing type example for each device are presented. Fig 4 compares the power spectral densities of the selected noises. The SM58 responses show the effects of mechanical resonances indicating the coupling of a number of damped oscillatory systems. The first resonance appears at around 100 Hz, while the second is between 200 and 300 Hz although this varies between sounds. For the AT803b lapel microphone a similar behaviour is found, but with a slightly lower first resonance at around 60 Hz. The temporal response shows that AT803b’s handling noises persist for a shorter time as compared with the SM58. The iPhone impact sounds decay much more quickly than the other two devices, and there is very little energy at lower frequencies due to the presence of a high-pass filter, active by default on the device with a cut-off frequency of about 120 Hz. The spectrum also indicates some resonant behaviour but at a higher frequency of around 1.2 kHz. Examining the response to rubbing excitation shows a noise like broadband response for the AT803b and iPhone devices, while the SM58 retains the resonant behaviour demonstrated by the tapping excitations. Figs 5 and 6 show spectrograms for the same sounds. The waveform level has been normalised to maximum absolute sample value, and 3 ms (128 samples) Hanning windows with 75% overlap were used. The rubbing sounds in Fig 6 show a consistent spectral distribution over time, while the tapping sounds in Fig 5 shows that higher frequencies are attenuated more quickly than lower frequencies.


Microphone Handling Noise: Measurements of Perceptual Threshold and Effects on Audio Quality.

Kendrick P, Jackson IR, Fazenda BM, Cox TJ, Li FF - PLoS ONE (2015)

Example spectrograms of rubbing type microphone handling noises.Recorded on the a) SM58, the b) iPhone and the c) AT803b.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608586&req=5

pone.0140256.g006: Example spectrograms of rubbing type microphone handling noises.Recorded on the a) SM58, the b) iPhone and the c) AT803b.
Mentions: Figs 1–3 display the waveforms of six of the handling noise examples for the times selected by the automatic segmentation algorithm. Each waveform is normalised to maximum absolute level and one impact and one rubbing type example for each device are presented. Fig 4 compares the power spectral densities of the selected noises. The SM58 responses show the effects of mechanical resonances indicating the coupling of a number of damped oscillatory systems. The first resonance appears at around 100 Hz, while the second is between 200 and 300 Hz although this varies between sounds. For the AT803b lapel microphone a similar behaviour is found, but with a slightly lower first resonance at around 60 Hz. The temporal response shows that AT803b’s handling noises persist for a shorter time as compared with the SM58. The iPhone impact sounds decay much more quickly than the other two devices, and there is very little energy at lower frequencies due to the presence of a high-pass filter, active by default on the device with a cut-off frequency of about 120 Hz. The spectrum also indicates some resonant behaviour but at a higher frequency of around 1.2 kHz. Examining the response to rubbing excitation shows a noise like broadband response for the AT803b and iPhone devices, while the SM58 retains the resonant behaviour demonstrated by the tapping excitations. Figs 5 and 6 show spectrograms for the same sounds. The waveform level has been normalised to maximum absolute sample value, and 3 ms (128 samples) Hanning windows with 75% overlap were used. The rubbing sounds in Fig 6 show a consistent spectral distribution over time, while the tapping sounds in Fig 5 shows that higher frequencies are attenuated more quickly than lower frequencies.

Bottom Line: Other factors such as noise type or background noise in the listening environment did not significantly affect quality ratings.Podcast, microphone type and reproduction equipment were found to be significant but only to a small extent.The SNR threshold at which 50% of subjects noticed handling noise was found to be 4.2 ± 0.6 dBA.

View Article: PubMed Central - PubMed

Affiliation: Acoustics Research Centre, School of Computing Science and Engineering, University of Salford, Salford, M5 4WT, United Kingdom.

ABSTRACT
A psychoacoustic experiment was carried out to test the effects of microphone handling noise on perceived audio quality. Handling noise is a problem affecting both amateurs using their smartphones and cameras, as well as professionals using separate microphones and digital recorders. The noises used for the tests were measured from a variety of devices, including smartphones, laptops and handheld microphones. The signal features that characterise these noises are analysed and presented. The sounds include various types of transient, impact noises created by tapping or knocking devices, as well as more sustained sounds caused by rubbing. During the perceptual tests, listeners auditioned speech podcasts and were asked to rate the degradation of any unwanted sounds they heard. A representative design test methodology was developed that tried to encourage everyday rather than analytical listening. Signal-to-noise ratio (SNR) of the handling noise events was shown to be the best predictor of quality degradation. Other factors such as noise type or background noise in the listening environment did not significantly affect quality ratings. Podcast, microphone type and reproduction equipment were found to be significant but only to a small extent. A model allowing the prediction of degradation from the SNR is presented. The SNR threshold at which 50% of subjects noticed handling noise was found to be 4.2 ± 0.6 dBA. The results from this work are important for the understanding of our perception of impact sound and resonant noises in recordings, and will inform the future development of an automated predictor of quality for handling noise.

No MeSH data available.