Level-Normalization of Feature Films Using Loudness vs Speech

Skovenborg E, Lund T. Level-Normalization of Feature Films Using Loudness vs Speech. In: AES Convention 135. Audio Engineering Society; 2013. Paper 8983. Available from: https://aes.org/publications/elibrary-page/?id=17031

@inproceedings{Skovenborg2013_17031,
  author = {Skovenborg, Esben and Lund, Thomas},
  title = {{Level-Normalization of Feature Films Using Loudness vs Speech}},
  booktitle = {AES Convention 135},
  note = {Paper 8983},
  year = {2013},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17031}
}

TY  - CPAPER
TI  - Level-Normalization of Feature Films Using Loudness vs Speech
AU  - Skovenborg, Esben
AU  - Lund, Thomas
T2  - AES Convention 135
M1  - Paper 8983
PY  - 2013
DA  - 2013/10/06
UR  - https://aes.org/publications/elibrary-page/?id=17031
PB  - Audio Engineering Society
LA  - en
AB  - We present an empirical study of the differences between level-normalization of feature films using the two dominant methods: loudness normalization and speech (“dialog”) normalization. The sound of 35 recent “blockbuster” DVDs were analyzed using both methods. The difference in normalization level was up to 14 dB, on average 5.5 dB. For all films the loudness method provided the lowest normalization level and hence the greatest headroom. Comparison of automatic speech measurement to manual measurement of dialog anchors shows a typical difference of 4.5 dB, with the automatic measurement producing the highest level. Employing the speech-classifier to process rather than measure the films, a listening test suggested that the automatic measure is positively biased because it sometimes fails to distinguish between “normal speech” and speech combined with “action” sounds. Finally, the DialNorm values encoded in the AC-3 streams on DVDs were compared to both the automatically and the manually measured speech levels and found to match neither one well. AES 135th Convention Best Peer-Reviewed Paper Award Cowinner
ER  -