AES New York 2011
Engineering Brief EB5

EB5 - Perception

Sunday, October 23, 2:15 pm — 3:00 pm (Room: 1E09)

EB5-1 Consumer Attitudes Toward Digital Audio QualityAinslie Harris, Robert Gordon University - Aberdeen, Scotland, UK
This paper builds upon an engineering brief submitted to the 130th AES Convention (Harris 2011). Where the May 2011 brief outlined initial findings from focus groups that were conducted, considering questions about preferred audio quality from the point of view of attitudes and consumer behavior, this brief focuses on an outline for future research, discussing important questions for consideration, and proposed methodology.
Engineering Brief 37 (Download now)

EB5-2 The Effect of Downmixing on Measured LoudnessScott G. Norcross, Michel C. Lavoie, Communications Research Center - Ottawa, Ontario, Canada
ITU-R BS.1770 has become the standard for loudness measurement in broadcasting. The measurement algorithm is equally adapted to 5.1 channel audio signals as it to a 2-channel downmix. Due to the manner by which the channels are summed, loudness differences can occur between the 5.1 channel signal and that of the stereo downmix. These differences are dependent on the inter-channel characteristics of the 5-channel mix. This engineering brief will outline the differences that can occur with different signals and provide data using real-world broadcast signals.
Engineering Brief 38 (Download now)

EB5-3 Prediction of Valence and Arousal from Music FeaturesAlbertus den Brinker, Ralph van Dinther, Philips Research Laboratories Eindhoven - Eindhoven, The Netherlands; J. Skowronek, Technical University Berlin - Berlin, Germany
Mood is an important attribute of music, and knowledge on mood can be used as a basic ingredient in music recommender and retrieval systems. Moods are assumed to be dominantly determined by two dimensions: valence and arousal. An experiment was conducted to attain data for song-based ratings of valence and arousal. It is shown that subject-averaged valence and arousal can be predicted from music features by a linear model.
Engineering Brief 39 (Download now)

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