Antes de pagar uma grana por um fonezão de marca famosa, considere o mais completo teste sobre o tema já feito. (Você vai economizar, isso é garantido.)
Por Jason Cross; Traduzido por Amanda Guizzo Zampieri
26 Junho 2017
Dizem que você recebe pelo quanto pagou. Ouvimos isso tantas vezes que já aceitamos como verdade. Mas quando o assunto é fones de ouvido, amigo, o que você paga nem sempre tem a ver com o que você leva pra casa.
Veja os achados de um estudo recém-publicado no The Journal of the Acoustical Society of America. Por meio de variados métodos, ele mediu a resposta de frequência de vários fones de ouvido a fim de determinar se há alguma correlação entre preço, tipo do fone de ouvido e a resposta de frequência. As descobertas falam por si mesmas. "Os resultados indicam que a nem a resposta medida, nem a tentativa de quantificar a qualidade percebida estão relacionadas ao preço", afirma o estudo.
Claro, não é a primeira vez que a ciência mostrou que um equipamento de som caro não é necessariamente o melhor. Quase nunca ouvimos falar disso porque os audiófilos que escrevem sobre equipamentos quase sempre são patologicamente avessos aos testes cegos.
A Home Theater Review publicou um artigo interessante sobre esse problema anos atrás. A Audio Engineering Society (Sociedade de Engenharia de Áudio) escreveu em 1994 que pesquisadores em Harmon, nos EUA, que queriam descobrir se as pessoas classificaram a qualidade dos fones de ouvido de modo diferente se pudessem vê-los. (Spoiler: sim.) Trata-se de um problema com os equipamentos de áudio – somos todos suscetíveis a um viés de confirmação. Se nos deparamos com um dispositivo que parece chique, ou que sabemos ser caro, isso vai maquiar nossa impressão da qualidade.
Testes de audição às cegas já foram tema de muito debate na comunidade de audiófilos; alguns gurus do som com frequência aparecem com desculpas duvidosas a respeito dos motivos para sua invalidação (quase sempre combinadas com uma completa falta de entendimento sobre o que significa um teste duplo-cego). Claro, é possível encontrar vários artigos implorando para que você não gaste dinheiro com cabos. A CNET tem um ótimo. O cético James Randi uma vez ofereceu um milhão de dólares a quem pudesse provar a superioridade de cabos de áudio de 7.250 dólares (ninguém conseguiu). Mas é difícil encontrar visões que não sejam enviesadas sobre caixas de som ou fones de ouvido.
O teste mais recente foi único a dar um passo adiante nos testes duplo-cegos de audição. Mediu a resposta de som com um método que conseguiu remover completamente as preferências de audição humana e as falhas. Os pesquisadores utilizaram uma cabeça especial acusticamente modelada com orelhas artificiais, embutidas com microfones, de modo a obter uma medição precisa, mas não específica de ouvinte para ver o quão variados são os fones de ouvido quando colocados em uma cabeça (com todas as mudanças acústicas implicadas).
Os fones de ouvido internos, em média, têm melhor resposta de baixo do que os supra-aurais (que ficam na orelha) ou circumaurais (sobre a orelha). E os supra-aurais têm resposta de frequência menos precisa do que os fones internos ou sobre as orelhas
Eles encontraram diferenças impressionantes. Os fones de ouvido internos, em média, têm melhor resposta de baixo do que os supra-aurais (que ficam na orelha) ou circumaurais (sobre a orelha). E os supra-aural têm uma resposta de frequência menos precisa do que os fones internos ou sobre as orelhas. Não importa o tipo, o preço dos fones não é um fator determinante em sua capacidade de reproduzir sons adequadamente.
Isso não quer dizer que você deve comprar os fones mais baratos que encontrar por aí. Alguns fatores que não foram medidos pelos pesquisadores, como qualidade de fabricação e o conforto, são muito importantes. Mas antes de gastar uma grana fodida, pergunte-se: estou pagando pela marca? É tudo marketing? Se o que esses fones de ouvido têm é uma propaganda cara, talvez você esteja pagando por ela, não pelo produto. Melhor dar uma olhada por aí antes de comprar.
No correlation between headphone frequency response and retail price
The Journal of the Acoustical Society of America 141, EL526 (2017);
ABSTRACT
This study quantifies variability of measured headphone response patterns and aims to uncover any correlations between headphone type, retail price, and frequency response. For this purpose, the mean, variance, and covariance of the frequency magnitude responses were analyzed and correlated with headphone type and retail value. The results indicate that neither the measured response nor an attempt to objectively quantify perceived quality is related to price. On average, in-ear headphones have a slightly higher measured bass response than circumaural and supra-aural headphones. Furthermore, in-ear and circumaural headphones have a slightly lower deviation from an assumed target curve than supra-ear models. Ninety percent of the variance across all headphone measurements can be described by a set of six basis functions. The first basis function is similar to published target responses, while the second basis function represents a spectral tilt.
1. INTRODUCTION
The market for headphones and earphones has been growing steadily over the last decade, both in value and volume. Market reports indicate a world-wide aftermarket reaching over 11 × 109 units sold per year (Iyer and Jelisejeva, 2016
7. Iyer, R., and Jelisejeva, Z. (2016). Headphones Market Report—Worldwide Outlook.
), with growth rates of up to 55% in the 25–49 USD segment. Research suggests that factors influencing consumers' choice as to which model to purchase are mostly based on wireless functionality (Iyer and Jelisejeva, 2016
7. Iyer, R., and Jelisejeva, Z. (2016). Headphones Market Report—Worldwide Outlook.
) and attributes such as shape, design, and comfort (Jensen et al., 2016
8. Jensen, R., Lauridsen, N., Poulsen, A., Tofle, C., and Christensen, F. (2016). “ Analysis of subjective evaluation of user experience with headphones,” in Proceedings of the AES Conference on Headphone Technology, Aalborg, Denmark.
). Interestingly, sound quality does not seem to be a major attribute for purchase decisions.
Objective assessments and subjective metrics for sound quality on headphones have been a subject of research, in particular over the last 5 years. Work by various authors has indicated that the subjective quality is mostly correlated with linear (spectral) attributes instead of non-linear (distortion) metrics (cf. Temme et al., 2014
13. Temme, S., Olive, S. E., Tatarunis, S., Welti, T., and McMullin, E. (2014). “ The correlation between distortion audibility and listener preference in headphones,” in Proceedings of the 137rd AES Convention, Convention Paper 9118, Los Angeles, CA.
; Fleischmann et al., 2014
3. Fleischmann, F., Estrella, J., and Plogsties, J. (2014). “ A method for comparison of non-linearities of consumer earphones using equalized stimuli,” in Proceedings of the 136th AES Convention, Convention e-brief 141, Berlin, Germany.
). In particular, research suggests that the frequency (magnitude) response is a major factor in listener preference scores (Olive and Welti, 2012
9. Olive, S. E., and Welti, T. (2012). “ The relationship between perception and measurement of headphone sound quality,” in Proceedings of the 133rd AES Convention, Convention Paper 8744, San Francisco, CA.
; Fleischmann et al., 2012
2. Fleischmann, A., Silzle, F., and Plogsties, J. (2012). “ Identification and evaluation of target curves for headphones,” in Proceedings of the 133rd AES Convention, Convention Paper 8740, San Francisco, CA.
; Olive et al., 2013
12. Olive, S. E., Welti, T., and McMullin, E. (2013). “ Listener preference for different headphone target response curves,” in Proceedings of the 134th AES Convention, Convention Paper 8867, Rome, Italy.
), and one headphone can effectively be transformed into another one by means of headphone equalization (Welti et al., 2016
14. Welti, T., Olive, S. E., and Khonsaripour, O. (2016). “ Validation of a virtual in-ear headphone listening test method,” in Proceedings of the 141st AES Convention, Convention Paper 9658, Los Angeles, CA.
). The preferred response however seems to be listener, content, and headphone dependent (Olive and Welti, 2015
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
; Olive et al., 2016
11. Olive, S. E., Welti, T., and Khonsaripour, O. (2016). “ The preferred low frequency response of in-ear headphones,” in Proceedings of the AES Conference on Headphone Technology, Aalborg, Denmark.
). Nevertheless, if one aims for a one-fits-all target response, diffuse field or free-field responses seem to be less preferred than a response based on measurements of a calibrated loudspeaker system in a listening room (Fleischmann et al., 2012
2. Fleischmann, A., Silzle, F., and Plogsties, J. (2012). “ Identification and evaluation of target curves for headphones,” in Proceedings of the 133rd AES Convention, Convention Paper 8740, San Francisco, CA.
; Olive et al., 2013
12. Olive, S. E., Welti, T., and McMullin, E. (2013). “ Listener preference for different headphone target response curves,” in Proceedings of the 134th AES Convention, Convention Paper 8867, Rome, Italy.
).
The body of research work covering headphone responses and target curves is typically based on a relatively small set of headphones. Therefore, variance patterns of headphone responses and their dependencies on headphone type and retail price are difficult to uncover. The aim of this paper is to reveal such patterns across a large set of headphones and to investigate whether and how these response patterns vary with price and headphone type.
2. METHOD
Headphone spectral magnitude responses were measured on a HMS II.3 artificial head (HEAD Acoustics, Germany) equipped with a type 3.3, open ear canal artificial ear (ITU-T P.380 2003
6. ITU-T P. 380. (2003). “ Electro-acoustic measurements on headsets,” International Telecommunications.
). Each headphone was reseated at least five times. A 5.46-s log sine sweep covering a frequency range between 20 Hz and 20 kHz was converted to electrical signals by a Fireface UC (RME, Germany) sound card using the headphone output connector. Log sine sweeps rather than linear sine sweeps were employed to allow verification that non-linear distortion components were virtually absent. The responses for both the left and right channel were measured with a sampling rate of 48 kHz. The 10+ measurements were averaged in the spectral power density domain using a discrete Fourier transform. Subsequently, the averaged response was converted to the log domain, and resampled to a perceptually-relevant frequency distribution between 20 Hz and 19 kHz with 0.1 equivalent rectangular bandwidth resolution [cf. Glasberg and Moore (1990)
5. Glasberg, B. R., and Moore, B. C. J. (1990). “ Derivation of auditory filter shapes from notched-noise data,” Hear. Res. 47, 103–138. https://doi.org/10.1016/0378-5955(90)90170-T
] using linear interpolation. After interpolation, the mean response across frequency was subtracted. In total, measurements for 283 headphones [in-ear (IE), supra-aural (SA), and circumaural (CA)] were acquired. Headphone retail prices were determined using Google shopping in Australia and converted to USD using a fixed exchange rate of 0.75. All headphones were categorized according to retail price quantile (with cutoffs of 25% and 75%) and headphone type (CA, SA, and IE).
3. RESULTS
3.1 Mean and variance
The mean and variance of the headphone response curves as a function of frequency are shown in Fig. 1. The solid black line within the gray area represents the arithmetic mean, and the gray area denotes the standard deviation. The top panels group the data into quantiles according to retail price; the bottom panels represent CA, IE, and SA headphones, for the left, middle, and right panel, respectively. The variable N in the panel titles presents the number of headphones in each category. The text inset in each panel denotes the root-mean-square deviation (RMSD) for each category across all frequencies, and for frequencies below 100 Hz only.
Fig. 1.
Mean and variance of headphone response curves as a function of frequency. Top panels: lower, middle, and upper price quantiles. Bottom panels: CA, IE, and SA headphones. RMSD denotes the root-mean-square deviation across frequency (full frequency axis and below 100 Hz only).
Across all groups, the averaged responses demonstrate a resonant peak at around 3.5 kHz, a secondary resonance at 10 kHz, and a general decrease in response toward 19 kHz. The average response does not seem to differ much across categories, with the exception of the IE headphones demonstrating an amplified low-frequency response and a wider resonance around 2–5 kHz (bottom-middle panel of Fig. 1). The higher low-frequency magnitude response for IE headphones could be resulting from an acoustic seal between an insert headset and an artificial ear greater than that typically occurring for humans. In other words, listeners may not necessarily perceive IE headphones as on average having a greater bass response.
The variance and the wide-band deviation across headphones (RMSD) decrease slightly with increasing price (RMSD of 4.3 and 3.5 dB for the lower and upper 25% quantile, respectively). The largest decrease in variance with price is observed for frequencies below 100 Hz, with the highest 25% price quantile having less than half the RMSD of the lowest 25% price quantile.
3.2 PCA
A principal component analysis (PCA) was performed across the headphone responses to investigate how response patterns correlate across frequency. The first six eigenvectors are shown in the left panel of Fig. 2, along with the cumulative percentage of variance accounted for. The right panels show the corresponding eigenvalue vs price for each headphone. Different symbols represent different headphone types (see the legend for Fig. 2).
Fig. 2.
Left panel: first six principal component eigenvectors and accumulative variance accounted for. Right panels: eigenvalue vs retail price for each headphone under test; top to bottom panels represent eigenvector one to six, respectively. Different symbols represent different headphone types (SA, IE, CA).
The first eigenvector is identical to the mean response across headphones. The corresponding eigenvalues are all positive while their magnitude deviates considerably across headphones. The second eigenvector seems to reflect an overall spectral tilt, with eigenvalues roughly centered around zero. The third to sixth eigenvectors seem to mostly model the position and magnitude of high-frequency peaks and notches. The corresponding eigenvalues are scattered around zero. Most interestingly, none of the eigenvalues seem to correlate strongly with the retail price, nor does there seem to exist any clear dependencies on headphone type. The results also indicate that 90% of the headphone response variance can be accounted for by six eigenvectors. This result supports the notion that headphone equalization may be realized by means of a small set of basis functions (Fleischmann et al., 2013
4. Fleischmann, F., Plogsties, J., and Neugebauer, B. (2013). “ Design of a headphone equalizer control based on principal component analysis,” in Proceedings of the 134th AES Convention, Convention Paper 8869, Rome, Italy.
).
3.3 Deviation from a target curve
Root-mean square errors (RMSEs) were calculated across frequency for each headphone with respect to an assumed target curve to assess an objective quality metric. Two target curves were used: the overall mean curve across all measured headphones, and the target curve suggested by Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
. The two target curves are shown in the top panel of Fig. 3; the curve from Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
was lowered by 6.5 dB to facilitate easier comparison between the two. When comparing the two target curves, the largest difference between the two seems to exist between 50 Hz and 2 kHz, for which the average headphone response is up to 5 dB higher than the target curve suggested by Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
, in particular around 100–200 Hz. The scatter plots of RMSE against retail price (lower panels of Fig. 3) are nevertheless very similar in the sense that none of them demonstrates a high correlation between price and deviation from the target curve. The numerical value between brackets in the inset of the lower panels denotes the Pearson correlation coefficient between RMSE and price. The IE headphones demonstrate the largest (absolute) correlation but its magnitude is nevertheless very small.
Fig. 3.
Top panel: target curve adopted from Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
(solid line) and average headphone response (dashed line). Bottom-left panel: RMSE as a function of price for all headphones using Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
as target function. Bottom-right panel: same as bottom-left panel, based on mean headphone response as target curve. Different symbols represent different headphone types (SA, IE, CA). Numerical values in the legend represent the Pearson correlation coefficient between RMSE and the retail price for each headphone type.
To further establish the significance of headphone type and RMSE, a two-way analysis of variance (ANOVA) was conducted with headphone type and RMSE as independent factors, and price as a dependent variable. These tests were based on a single target curve for all three headphone types (Olive and Welti, 2015
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
). The effect of headphone type was shown to be significant [ANOVA F statistic F(279, 2) = 21.05; significance level or probability of falsely rejecting the Null hypothesis p < 0.0001] while the effect of RMSE was not [F(279, 1) = 2.59; p > 0.10]. A post hoc comparison of mean RMSEs across headphone types was performed using a non-parametric permutation test and the median as location statistic. Bonferroni-adjusted p values indicated that the RMSEs for CA and IE headphones are on average significantly smaller than those for SA headphones (p < 0.025).
4. CONCLUSIONS
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Based on the evaluation of the mean, variance, PCA, and mean square error with respect to a target function, no correlation could be observed between the measured magnitude response and retail price of headphones. However, the variance in low-frequency response seems to decrease with increasing price, indicating an improved bass response measurement consistency across headphones in the higher price range. It is however unclear whether this improved consistency with a higher retail price is the result of better headphones or better repeatability of measurements with more expensive models. Nevertheless, assuming that the perceived audio quality is largely determined by the spectral magnitude response of headphones, there are plenty of relatively cheap models that match the assumed target function, as well as very expensive ones that deviate significantly from an assumed ideal response.
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On average, IE headphone measurements demonstrate slightly more bass than CA and SA headphones. The difference amounts to approximately 4 dB at 100 Hz. This finding is in line with subjective preference ratings reported by Olive et al. (2016)
11. Olive, S. E., Welti, T., and Khonsaripour, O. (2016). “ The preferred low frequency response of in-ear headphones,” in Proceedings of the AES Conference on Headphone Technology, Aalborg, Denmark.
. The observed higher bass response could be resulting from a greater seal between headphones and artificial ear compared to the seal for human ears (ITU-T P.380, 2003
6. ITU-T P. 380. (2003). “ Electro-acoustic measurements on headsets,” International Telecommunications.
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CA and IE headphones have a response that (on average) more closely mimics the target curve proposed by Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
than SA headphones.
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The target function suggested by Olive and Welti (2015)
10. Olive, S. E., and Welti, T. (2015). “ Factors that influence listeners' preferred bass and treble balance in headphones,” in Proceedings of the 139th AES Convention, Convention Paper 9382, New York, NY.
is fairly similar to the average headphone response found in this study, with the exception of a deviation of up to about 5 dB for frequencies between 50 Hz and 2 kHz.
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PCA can account for 90% of the variance across all measured headphones with six eigenvectors. The first eigenvector is similar to published target responses, while the second eigenvector represents a global spectral tilt.
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5. LIMITATIONS
First, all analyses were based on magnitude responses only, discarding phase or non-linear attributes. Research has suggested that non-linear properties of headphone responses have little effect and no significant traces of non-linear behavior were observed in this study. However the author is not aware of any work on the perceptual implications of the phase response of headphones. A second limitation is that a specific target curve was assumed, irrespective of the headphone type, audio content, listener demographics, or influence of environmental sounds. Third, it is well known that dummy-head measurements for headphones can be inaccurate at low and high frequencies and deviate from human ear canal measurements (ITU-T P.380, 2003
6. ITU-T P. 380. (2003). “ Electro-acoustic measurements on headsets,” International Telecommunications.
; Christensen et al., 2013
1. Christensen, A. T., Hess, W., Silzle, A., and Hammershøi, D. (2013). “ Magnitude and phase response measurement of headphones at the eardrum,” in Proceedings of the 51st AES International Conference, Helsinki, Finland.
). Repeated measurements for reseated headphones should partly alleviate this problem. However, it could be that measurement inaccuracies have some implications for the results and conclusions drawn in this paper.
REFERENCES AND LINKS
- 1. Christensen, A. T., Hess, W., Silzle, A., and Hammershøi, D. (2013). “ Magnitude and phase response measurement of headphones at the eardrum,” in Proceedings of the 51st AES International Conference, Helsinki, Finland. Google Scholar
- 2. Fleischmann, A., Silzle, F., and Plogsties, J. (2012). “ Identification and evaluation of target curves for headphones,” in Proceedings of the 133rd AES Convention, Convention Paper 8740, San Francisco, CA. Google Scholar
- 3. Fleischmann, F., Estrella, J., and Plogsties, J. (2014). “ A method for comparison of non-linearities of consumer earphones using equalized stimuli,” in Proceedings of the 136th AES Convention, Convention e-brief 141, Berlin, Germany. Google Scholar
- 4. Fleischmann, F., Plogsties, J., and Neugebauer, B. (2013). “ Design of a headphone equalizer control based on principal component analysis,” in Proceedings of the 134th AES Convention, Convention Paper 8869, Rome, Italy. Google Scholar
- 5. Glasberg, B. R., and Moore, B. C. J. (1990). “ Derivation of auditory filter shapes from notched-noise data,” Hear. Res. 47, 103–138. https://doi.org/10.1016/0378-5955(90)90170-T, Google ScholarCrossref, CAS
- 6. ITU-T P. 380. (2003). “ Electro-acoustic measurements on headsets,” International Telecommunications. Google Scholar
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- 11. Olive, S. E., Welti, T., and Khonsaripour, O. (2016). “ The preferred low frequency response of in-ear headphones,” in Proceedings of the AES Conference on Headphone Technology, Aalborg, Denmark. Google Scholar
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- 13. Temme, S., Olive, S. E., Tatarunis, S., Welti, T., and McMullin, E. (2014). “ The correlation between distortion audibility and listener preference in headphones,” in Proceedings of the 137rd AES Convention, Convention Paper 9118, Los Angeles, CA. Google Scholar
- 14. Welti, T., Olive, S. E., and Khonsaripour, O. (2016). “ Validation of a virtual in-ear headphone listening test method,” in Proceedings of the 141st AES Convention, Convention Paper 9658, Los Angeles, CA. Google Scholar
https://asa.scitation.org/doi/10.1121/1.4984044