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A. Travaglini, A. Alemanno, and A. Uncini, "HELM: High Efficiency Loudness Model for Broadcast Content," Paper 8612, (2012 April.). doi: A. Travaglini, A. Alemanno, and A. Uncini, "HELM: High Efficiency Loudness Model for Broadcast Content," Paper 8612, (2012 April.). doi:
Abstract: In this paper, we propose a new algorithm for measuring the loudness levels of broadcast content. It is called the High Efficiency Loudness Model (HELM) and it aims to provide robust measurement of programs of any genre, style and format, including stereo and multichannel audio 5.1 surround sound. HELM was designed taking into account the typical conditions of the home listening environment and it is therefore particularly good at meeting the needs of broadcast content users. While providing a very efficient assessment of typical generic programs, it also successfully approaches some issues that arise when assessing unusual content such as programs heavily based on bass frequencies, wide loudness range programs and multi-channel programs as opposed to stereo ones. This paper details the structure of HELM, including its channel-specific frequency weighting and recursive gating implementation. Finally, we present the results of a mean opinion score (MOS) subjective test that demonstrates the effectiveness of the proposed method.

@article{travaglini2012helm:,
author={travaglini, alessandro and alemanno, andrea and uncini, aurelio},
journal={journal of the audio engineering society},
title={helm: high efficiency loudness model for broadcast content},
year={2012},
volume={},
number={},
pages={},
doi={},
month={april},} @article{travaglini2012helm:,
author={travaglini, alessandro and alemanno, andrea and uncini, aurelio},
journal={journal of the audio engineering society},
title={helm: high efficiency loudness model for broadcast content},
year={2012},
volume={},
number={},
pages={},
doi={},
month={april},
abstract={in this paper, we propose a new algorithm for measuring the loudness levels of broadcast content. it is called the high efficiency loudness model (helm) and it aims to provide robust measurement of programs of any genre, style and format, including stereo and multichannel audio 5.1 surround sound. helm was designed taking into account the typical conditions of the home listening environment and it is therefore particularly good at meeting the needs of broadcast content users. while providing a very efficient assessment of typical generic programs, it also successfully approaches some issues that arise when assessing unusual content such as programs heavily based on bass frequencies, wide loudness range programs and multi-channel programs as opposed to stereo ones. this paper details the structure of helm, including its channel-specific frequency weighting and recursive gating implementation. finally, we present the results of a mean opinion score (mos) subjective test that demonstrates the effectiveness of the proposed method.},}

TY - paper
TI - HELM: High Efficiency Loudness Model for Broadcast Content
SP - EP -
AU - Travaglini, Alessandro
AU - Alemanno, Andrea
AU - Uncini, Aurelio
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - April 2012 TY - paper
TI - HELM: High Efficiency Loudness Model for Broadcast Content
SP - EP -
AU - Travaglini, Alessandro
AU - Alemanno, Andrea
AU - Uncini, Aurelio
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - April 2012
AB - In this paper, we propose a new algorithm for measuring the loudness levels of broadcast content. It is called the High Efficiency Loudness Model (HELM) and it aims to provide robust measurement of programs of any genre, style and format, including stereo and multichannel audio 5.1 surround sound. HELM was designed taking into account the typical conditions of the home listening environment and it is therefore particularly good at meeting the needs of broadcast content users. While providing a very efficient assessment of typical generic programs, it also successfully approaches some issues that arise when assessing unusual content such as programs heavily based on bass frequencies, wide loudness range programs and multi-channel programs as opposed to stereo ones. This paper details the structure of HELM, including its channel-specific frequency weighting and recursive gating implementation. Finally, we present the results of a mean opinion score (MOS) subjective test that demonstrates the effectiveness of the proposed method.