14 Paired comparisons: Some basic procedures and examples

Bradley, Ralph A.

doi:10.1016/s0169-7161(84)04016-5

Cited by 77 publications

(62 citation statements)

References 39 publications

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“…It was calculated at 0.026, indicating a statistically significant agreement among subjects [59]. We ranked the tones by the number of positive votes they received for each instrument and emotional category and derived scale values using the Bradley-Terry-Luce (BTL) statistical model [60,61].…”

Section: Ranking Results For the Emotional Characteristics With Diffementioning

confidence: 99%

“…Reverberation time (RT 60 ) is one of the most important characteristics of reverberation, and measures the time reverberation takes to decay by 60 dB SPL from an initial impulse [41]. Jordan [42] suggested an alternative measurement called Early Decay Time (EDT), which is defined as either: (1) six times the time interval that it takes for an impulse response to decay from 0 dB to -10 dB, or (2) by the straight line that best fits an impulse response as it decays from 0 dB to -10 dB.…”

Section: Reverberation 121 Artificial Reverberation Modelsmentioning

confidence: 99%

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The Effects of Reverberation on the Emotional Characteristics of Musical Instruments

Mo¹,

Wu²,

Horner³

2016

J. Audio Eng. Soc.

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Though previous research has shown the effects of reverberation on clarity, spaciousness, and other perceptual aspects of music, it is still largely unknown to what extent reverberation influences the emotional characteristics of musical instrument sounds. This paper investigates the effect of simple parametric reverberation on music emotion, in particular, the effect of reverberation length and amount. We conducted a listening test to compare the effect of reverberation on the emotional characteristics of eight instrument sounds representing the wind and bowed string families. We compared these sounds over eight emotional categories. We found that reverberation length and amount had a strongly significant effect on the emotional characteristics Romantic and Mysterious and a medium effect on Sad, Scary, and Heroic for the samples we tested. Interestingly, for Comic, reverberation length and amount had the opposite effect; that is, anechoic tones were judged most Comic. Reverb had a mild effect on Happy and relatively little effect on Shy. These results give audio engineers and musicians an interesting perspective on simple parametric artificial reverberation. INTRODUCTIONPrevious research has shown that musical instrument sounds have strong and distinctive emotional characteristics [1][2][3][4][5]. For example, that the trumpet is happier in character than the horn, even in isolated sounds apart from musical context.In light of this, one might wonder what effect reverberation has on the character of music emotion. This leads to a host of follow-up questions: Do all emotional characteristics become stronger with more reverberation? Or, are some emotional characteristics affected more and others less (e.g., positive emotional characteristics more, negative less)? In particular, what are the effects of reverberation time and amount? What are the effects of hall size and listener position? Which instruments sound emotionally stronger to listeners in the front or back of small and large halls? Are dry sounds without reverberation emotionally dry as well, or, do they have distinctive emotional characteristics?We cannot address all of the above questions definitively in this paper with only a simple parametric reverberator and a few parameter settings, but we can make a good start. This work will give audio engineers and musicians an interesting perspective on simple parametric artificial reverberation. More studies with different reverberation models and parameters should be carried out to get more definitive answers. Understanding how listeners perceive emotional characteristics in reverberation can help us engineer potentially even more expressive recordings and opens new possibilities for interactive music systems and applications. BACKGROUND Music Emotion and TimbrePrevious work has investigated emotion recognition in music, especially addressing melody [6], harmony [7,8], rhythm [9,10], lyrics [11], and localization cues [12]. Similarly, researchers have found timbre to be useful in a number of applications such ...

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Section: Ranking Results For the Emotional Characteristics With Diffementioning

confidence: 99%

Section: Reverberation 121 Artificial Reverberation Modelsmentioning

confidence: 99%

The Effects of Reverberation on the Emotional Characteristics of Musical Instruments

Mo¹,

Wu²,

Horner³

2016

J. Audio Eng. Soc.

View full text Add to dashboard Cite

show abstract

“…We ranked the sounds by the number of positive votes received for each emotional category, deriving scale values using the Bradley-Terry-Luce (BTL) statistical model [60,61]. The BTL values for each emotional category sum to 1.…”

Section: Resultsmentioning

confidence: 99%

“…For example, if all 28 sounds (14 notes × 2 dynamic levels) were considered equally Happy, the BTL scale values would be 1/28 ≈ 0.0357. The corresponding 95% confidence intervals were derived using Bradley's method [61]. Fig.…”

Section: Resultsmentioning

confidence: 99%

The Emotional Characteristics of Bowed String Instruments with Different Pitch and Dynamics

Chau¹,

Gilburt²,

Mo³

et al. 2017

J. Audio Eng. Soc.

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Previous research has shown that different musical instrument sounds have strong emotional characteristics. This paper investigates how emotional characteristics vary with pitch and dynamics within the bowed string instrument family. We conducted listening tests to compare the effects of pitch and dynamics on the violin, viola, cello, and double bass. Listeners compared the sounds pairwise over 10 emotional categories. Results showed that the emotional characteristics Happy, Heroic, Romantic, Comic, and Calm generally increased with pitch but decreased at the highest pitches. Angry and Sad generally decreased with pitch. Scary was strong in the extreme low and high registers, while Shy and Mysterious were unaffected by pitch. For dynamics, the results showed that Heroic, Comic, and Angry were stronger for loud notes, while Romantic, Calm, Shy, Sad, and the high register for Happy were stronger for soft notes. Scary and Mysterious were unaffected by dynamics. The results also showed significant differences between different bowed string instruments on notes of the same pitch and dynamic level. These results help quantify our understanding of the relative emotional characteristics of the strings. They provide audio engineers and musicians with suggestions for emphasizing emotional characteristics of the bowed strings in sound recordings and performances.

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“…This model has had a long tradition of being used to measure competitor strength in tournaments. Recent work that incorporated the BradleyTerry model for measuring competitor strength includes Bradley (1984), Joe (1990), and Glickman (1999). Letting Â i and Â j be the strength parameters of players i and j, and letting Y ij be 1 if i defeats j, and 0 if j defeats i, the Bradley-Terry model asserts…”

Section: Optimal Design For the Bradley-terry Modelmentioning

confidence: 99%

Adaptive paired comparison design

Glickman

Jensen

2005

Journal of Statistical Planning and Inference

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An important aspect of paired comparison experiments is the decision of how to form pairs in advance of collecting data. A weakness of typical paired comparison experimental designs is the di culty in incorporating prior information, which can be particularly relevant for the design of tournament schedules for players of games and sports. Pairing methods that make use of prior information are often ad hoc algorithms with little or no formal basis. The problem of pairing objects can be formalized as a Bayesian optimal design. Assuming a linear paired comparison model for outcomes, we develop a pairing method that maximizes the expected gain in Kullback-Leibler information from the prior to the posterior distribution. The optimal pairing is determined using a combinatorial optimization method commonly used in graph-theoretic contexts. We discuss the properties of our optimal pairing criterion, and demonstrate our method as an adaptive procedure for pairing objects multiple times. We compare the performance of our method on simulated data against random pairings, and against a system that is currently in use in tournament chess.

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14 Paired comparisons: Some basic procedures and examples

Cited by 77 publications

References 39 publications

The Effects of Reverberation on the Emotional Characteristics of Musical Instruments

The Effects of Reverberation on the Emotional Characteristics of Musical Instruments

The Emotional Characteristics of Bowed String Instruments with Different Pitch and Dynamics

Adaptive paired comparison design

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