Loudness Assessment of Musical Tones Equalized in A-weighted Level

Klonari, Despina; Pastiadis, Konstantinos; Papadelis, Georgios; Papanikolaou, George

doi:10.2478/v10168-011-0019-7

Cited by 7 publications

(5 citation statements)

References 15 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the TVL predicted that the mean loudness values for 95% of the amplitude-normalized excerpts were within a range of 4 phons. In comparison, Klonari, Pastiadis, Papadelis, and Papanikolaou (2011) reported that the range of subjective equal loudness judgements on a pair of stimuli corresponding to the same instrument (oboe) sounding the same pitch (A4) reached 3 dB (with the standard presented at 70 dBA), increasing to 5 dB when the pitch varied by as little as a major third. Given that a difference of 1 phon corresponds to a change of 1 dB for a 1000-Hz pure tone (Fastl & Zwicker, 2007), these results suggest that the amplitude-normalized excerpts used in our study were adequately matched for loudness.…”

Section: Discussionmentioning

confidence: 99%

Beyond Intensity: Spectral Features Effectively Predict Music-Induced Subjective Arousal

Gingras

Marin

Fitch

2014

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

Emotions in music are conveyed by a variety of acoustic cues. Notably, the positive association between sound intensity and arousal has particular biological relevance. However, although amplitude normalization is a common procedure used to control for intensity in music psychology research, direct comparisons between emotional ratings of original and amplitude-normalized musical excerpts are lacking. In this study, 30 nonmusicians retrospectively rated the subjective arousal and pleasantness induced by 84 six-second classical music excerpts, and an additional 30 nonmusicians rated the same excerpts normalized for amplitude. Following the cue-redundancy and Brunswik lens models of acoustic communication, we hypothesized that arousal and pleasantness ratings would be similar for both versions of the excerpts, and that arousal could be predicted effectively by other acoustic cues besides intensity. Although the difference in mean arousal and pleasantness ratings between original and amplitude-normalized excerpts correlated significantly with the amplitude adjustment, ratings for both sets of excerpts were highly correlated and shared a similar range of values, thus validating the use of amplitude normalization in music emotion research. Two acoustic parameters, spectral flux and spectral entropy, accounted for 65% of the variance in arousal ratings for both sets, indicating that spectral features can effectively predict arousal. Additionally, we confirmed that amplitude-normalized excerpts were adequately matched for loudness. Overall, the results corroborate our hypotheses and support the cue-redundancy and Brunswik lens models.

show abstract

Section: Discussionmentioning

confidence: 99%

Beyond Intensity: Spectral Features Effectively Predict Music-Induced Subjective Arousal

Gingras

Marin

Fitch

2014

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

show abstract

“…frequency parameters (e.g. a desired bandwidth) and amplitude, can be controlled, however as stated by Klonari et al (2011) the problem of loudness estimation and the control of musical sounds has long been an issue in various applications of audio engineering and technology.…”

Section: Methods Proposedmentioning

confidence: 99%

Loudness Scaling Test Based on Categorical Perception

Kostek

Odya

Suchomski

2016

Archives of Acoustics

View full text Add to dashboard Cite

The main goal of this research study is focused on creating a method for loudness scaling based on categorical perception. Its main features, such as: way of testing, calibration procedure for securing reliable results, employing natural test stimuli, etc., are described in the paper and assessed against a procedure that uses 1/2-octave bands of noise (LGOB) for the loudness growth estimation. The Mann-Whitney U-test is employed to check whether the proposed method is statistically equivalent to LGOB. It is shown that loudness functions obtained in both methods are similar in the statistical context. Moreover, the band-filtered musical instrument signals are experienced as more pleasant than the narrow-band noise stimuli and the proposed test is performed in a shorter time. The method proposed may be incorporated into fitting hearing strategies or used for checking individual loudness growth functions and adapting them to the comfort level settings while listening to music.

show abstract

“…The main purpose of SQ metrics is to replace jury tests with an acoustic measurement that would provide an accurate prediction of sound quality judgements made by human listeners. In recent studies several SQ metrics, such as loudness (Fastl, Zwicker, 2007;Klonari et al, 2011), sharpness (Leite et al, 2008;Wang et al, 2007), roughness (Aures, 1985a;Miśkiewicz et al, 2007;Szczepańska-Antosik, 2008; Vencovský, 2016), and tonality (Aures, 1985b;Cuddy et al, 2007;Terhardt et al, 1982) were developed and used for sound quality evaluation (Carletti, 2013; Pleban, 2010). The calculations of individual SQ metrics have been used to compute combined, overall measures of sound quality, such as pleasantness and unbiased annoyance (Kaczmarek, Preis, 2010).…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Pourseiedrezaei

Loghmani

Keshmiri

2021

View full text Add to dashboard Cite

The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.

show abstract

Loudness Assessment of Musical Tones Equalized in A-weighted Level

Cited by 7 publications

References 15 publications

Beyond Intensity: Spectral Features Effectively Predict Music-Induced Subjective Arousal

Beyond Intensity: Spectral Features Effectively Predict Music-Induced Subjective Arousal

Loudness Scaling Test Based on Categorical Perception

Archives of Acoustics

Contact Info

Product

Resources

About