Sensory Evaluation in Practice

Pedersen, Torben Holm; Zacharov, Nick

doi:10.1201/9780429429422-4

Cited by 3 publications

(11 citation statements)

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“…For instance, clarity resembles a dimension of the same label developed by Gabrielsson (1979) , Gabrielsson and Sjogren (1979) , and Gabrielsson et al (1988) , and another dimension with the same label from Toole (1985) and Lindau (2015) . Other comparable attributes include clear ( Choisel and Wickelmaier, 2007 ), definition ( Lokki et al, 2011 ), detailed ( Pedersen and Zacharov, 2015 ), and source separation ( Legarth et al, 2016 ). These attributes encapsulate a dimension of audio quality that reflects the distinguishability and separability of sound elements or sources, and the current sensory panel highlighted the importance of this quality in the context of music listening and hearing loss.…”

Section: Discussionmentioning

confidence: 99%

“…These attributes encapsulate a dimension of audio quality that reflects the distinguishability and separability of sound elements or sources, and the current sensory panel highlighted the importance of this quality in the context of music listening and hearing loss. It is worth noting that the terms used by participants to develop clarity resemble some adjacent attributes in the literature, such as clean ( Pedersen and Zacharov, 2015 ) and crispness ( Uys et al, 2012 ); these attributes refer to terms like ‘dull’, ‘blurred’, ‘distinct’ and ‘muddy’, but do not specifically highlight the experience of distinguishing between sound elements in the audio in their definitions.…”

Section: Discussionmentioning

confidence: 99%

“…For FG3 participants were given the word cloud from FG2, visualising perceptual terms used four or more times in the elicitation task and highlighting in red those that were not retained in FG1. In addition, as the development of definitions was anticipated to be difficult in some cases, definitions for similar perceptual attributes in existing literature were prepared ( Gabrielsson, 1979 ; Toole, 1985 ; Zacharov and Koivuniemi, 2001 ; Berg and Rumsey, 2003 ; Choisel and Wickelmaier, 2007 ; Souza et al, 2007 ; Lokki et al, 2011 ; Uys et al, 2012 ; Lindau, 2015 ; Pedersen and Zacharov, 2015 ; Legarth et al, 2016 ), in case discussions required support. Finally, across all focus group discussions the research team reviewed the data, discussions, and existing literature ( Jekosch, 2004 ; Lorho, 2010 ; Brunnström et al, 2013 ; Möller and Raake, 2014 ; Pike, 2019 ), to develop a working definition of music audio quality and rating scale structure; this was proposed in FG3 for feedback from participants.…”

Section: Focus Groupmentioning

confidence: 99%

“…Results suggest that the perceptual attribute space of audio quality can be divided into four categories: Timbral (e.g., brightness, tone colour, richness) , spatial (e.g., depth, reverberation, width, distance) , defects (e.g., noise, distortion, hiss, hum), and quality (e.g., realism, fidelity, dynamics, stability). These categories are further reflected in a recent ‘sound wheel’ lexicon of audio quality developed by Pedersen and Zacharov (2015) , utilising attributes from existing literature and working with expert panels to identify mappings and clusters of attributes. This model notes timbre, spatial and artefacts as three overarching perceptual groupings, reflecting the work by Berg and Rumsey (2003) and Le Bagousse et al (2014) .…”

Section: Introductionmentioning

confidence: 99%

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Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users

Bannister,

Greasley,

Cox

et al. 2024

Front. Psychol.

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IntroductionPrevious work on audio quality evaluation has demonstrated a developing convergence of the key perceptual attributes underlying judgments of quality, such as timbral, spatial and technical attributes. However, across existing research there remains a limited understanding of the crucial perceptual attributes that inform audio quality evaluation for people with hearing loss, and those who use hearing aids. This is especially the case with music, given the unique problems it presents in contrast to human speech.MethodThis paper presents a sensory evaluation study utilising descriptive analysis methods, in which a panel of hearing aid users collaborated, through consensus, to identify the most important perceptual attributes of music audio quality and developed a series of rating scales for future listening tests. Participants (N = 12), with a hearing loss ranging from mild to severe, first completed an online elicitation task, providing single-word terms to describe the audio quality of original and processed music samples; this was completed twice by each participant, once with hearing aids, and once without. Participants were then guided in discussing these raw terms across three focus groups, in which they reduced the term space, identified important perceptual groupings of terms, and developed perceptual attributes from these groups (including rating scales and definitions for each).ResultsFindings show that there were seven key perceptual dimensions underlying music audio quality (clarity, harshness, distortion, spaciousness, treble strength, middle strength, and bass strength), alongside a music audio quality attribute and possible alternative frequency balance attributes.DiscussionWe outline how these perceptual attributes align with extant literature, how attribute rating instruments might be used in future work, and the importance of better understanding the music listening difficulties of people with varied profiles of hearing loss.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Focus Groupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users

Bannister,

Greasley,

Cox

et al. 2024

Front. Psychol.

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“…The well-known coarticulatory effects by which both perception and the acoustic content of speech sounds are influenced by the properties of the surrounding phonetic segments (see Diehl, Lotto & Holt 2004) are hence not accounted for in the SiP test. However, attempting to control for all occurring coarticulatory effects would require the SiP test to include many more MVGs, which would ultimately lead to very lengthy testing times and possible listening fatigue (see Pedersen & Zacharov 2018).…”

Section: Discussionmentioning

confidence: 99%

The development of linguistic stimuli for the Swedish Situated Phoneme test

2021

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The speech perception ability of people with hearing loss can be efficiently measured using phonemic-level scoring. We aimed to develop linguistic stimuli suitable for a closed-set phonemic discrimination test in the Swedish language called the Situated Phoneme (SiP) test. The SiP test stimuli that we developed consisted of real monosyllabic words with minimal phonemic contrast, realised by phonetically similar phones. The lexical and sublexical factors of word frequency, phonological neighbourhood density, phonotactic probability, and orthographic transparency were similar between all contrasting words. Each test word was recorded five times by two different speakers, including one male and one female. The accuracy of the test-word recordings was evaluated by 28 normal-hearing subjects in a listening experiment with a silent background using a closed-set design. With a few exceptions, all test words could be correctly discriminated. We discuss the results in terms of content- and construct-validity implications for the Swedish SiP test.

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Sonic Interactions in Virtual Environments

Serafin

2024

Encyclopedia of Computer Graphics and Games

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Sensory Evaluation in Practice

Cited by 3 publications

References 0 publications

Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users

Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users

The development of linguistic stimuli for the Swedish Situated Phoneme test

Sonic Interactions in Virtual Environments

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