Clean Audio for TV broadcast: An Object-Based Approach for Hearing-Impaired Viewers

Shirley, Ben; Oldfield, Rob

doi:10.17743/jaes.2015.0017

Cited by 27 publications

(18 citation statements)

References 23 publications

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“…Research presented as part of the EU FP7 FascinatE project [22] also illustrated the potential for object-based clean audio for live events [23]. In the FascinatE project on-pitch sounds from an English Premier League football game, such as ball kicks and whistle blows, were captured as audio objects and transmitted to the reproduction system with positional metadata separate to crowd sounds and commentary streams.…”

Section: Object-based Audiomentioning

confidence: 99%

Personalized Object-Based Audio for Hearing Impaired TV Viewers

Shirley¹,

Meadows²,

Malak³

et al. 2017

J. Audio Eng. Soc.

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Section: Object-based Audiomentioning

confidence: 99%

Personalized Object-Based Audio for Hearing Impaired TV Viewers

Shirley¹,

Meadows²,

Malak³

et al. 2017

J. Audio Eng. Soc.

Self Cite

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“…It was found that approximately three quarters of listeners preferred the objectbased experience compared to traditional radio coverage. Other examples of object-based experiences include mix adaptation for hearing-impaired listeners [22], visual content adaptation to suit the user's profile [7] and adapting the length of audio content to suit the user's requirements [1].…”

Section: Object-based Audiomentioning

confidence: 99%

“…In the context of spatial audio evaluation, Rumsey et al [20] distinguish between background components consisting of diffuse or environment-related aspects of the scene, and foreground components consisting of localisable objects. In the context of television audio for hearing-impaired users, Shirley and Oldfield [22] propose three categories of audio objects-speech content whose comprehension is critical, background noise that has been shown to be detrimental to both clarity and to perceived overall sound quality, and other non-speech sounds that are considered important to comprehension and/or enjoyment of the material. In a more complex categorisation of broadcast audio objects, Woodcock et al [27] used hierarchical agglomerative clustering to identify seven general categories, which relate to sounds indicating actions and movement, continuous and transient background sound, clear speech, non-diegetic music and effects, sounds indicating the presence of people, and prominent attention grabbing transient sounds.…”

Section: Object-based Audiomentioning

confidence: 99%

Exploring object-based content adaptation for mobile audio

Walton

Evans²,

Kirk

et al. 2018

Pers Ubiquit Comput

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Mobile devices enable users to consume media with audio content in a wide range of contexts, with environmental noise being present in many of these. Several methods exist that aim to improve the experience of mobile listening by utilising information about the environmental noise, such as volume and dynamic range adaptation. This paper explores a fundamentally different approach to improving the mobile listening experience by using the object-based audio paradigm, where individual audio sources are mixed in response to each specific listening context. Three experimental studies, containing both quantitative and qualitative aspects, are presented which investigate whether environmental noise influences preference of background-foreground audio object balance in a mix. The results indicate that environmental noise can influence the preferred audio mix and that the nature of the adaptations made is dependent upon both audio content and user. Additionally, qualitative analysis provides an understanding of the role of environmental noise on preferred audio mix. It is believed that the content adaptation method explored in this paper is a simple yet useful tool for adapting content to suit both the context and the user.

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“…Shirley and Oldfield [18] introduced an approach for 'clean' audio in TV broadcast, which is compatible to ISO/IEC 23008 High Efficiency Coding and Media Delivery in Heterogeneous Environments. This object-based method allows the stream carrying the speech content to be separately transmitted; end users therefore can alter the SBR level to achieve better intelligibility for the speech content.…”

mentioning

confidence: 99%

“…This system enables renderer-listener interaction and permits listeners to control the mixing level for personalised intelligibility. Similar to [18], this approach essentially adjusts the gain of speech as a single object against the gain of other objects in the background. Further subjective rating experiments suggested that this system improved listeners' experience when auditioning speech content in background sounds.…”

mentioning

confidence: 99%

Automatic Speech-to-Background Ratio Selection to Maintain Speech Intelligibility in Broadcasts Using an Objective Intelligibility Metric

2018

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While mixing, sound producers and audio professionals empirically set the speech-tobackground ratio (SBR) based on rules of thumb and their own perception of sounds. There is no guarantee that the speech content will be intelligible for the general population consuming content over a wide variety of devices, however. In this study, an approach to automatically determine the appropriate SBR for a scene using an objective intelligibility metric is introduced. The model-estimated SBR needed for a preset minimum intelligibility level was compared to the listener-preferred SBR for a range of background sounds. It was found that an extra gain added to the model estimation is needed even for listeners with normal hearing. This gain is needed so an audio scene can be auditioned with comfort and without compromising the sound effects contributed by the background. When the background introduces little informational masking, the extra gain holds almost constant across the various background sounds. However, a larger gain is required for a background that induces informational masking, such as competing speech. The results from a final subjective rating study show that the model-estimated SBR with the additional gain, yields the same listening experience as the SBR preferred by listeners.

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Clean Audio for TV broadcast: An Object-Based Approach for Hearing-Impaired Viewers

Cited by 27 publications

References 23 publications

Personalized Object-Based Audio for Hearing Impaired TV Viewers

Personalized Object-Based Audio for Hearing Impaired TV Viewers

Exploring object-based content adaptation for mobile audio

Automatic Speech-to-Background Ratio Selection to Maintain Speech Intelligibility in Broadcasts Using an Objective Intelligibility Metric

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