Target bookends verification#

The difference amounts to approximately 4 dB at 100 Hz. On average, IE headphone measurements demonstrate slightly more bass than CA and SA headphones. 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. 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. 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.

The IE headphones demonstrate the largest (absolute) correlation but its magnitude is nevertheless very small.īased 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. The numerical value between brackets in the inset of the lower panels denotes the Pearson correlation coefficient between RMSE and price. 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 scatter plots of RMSE against retail price (lower panels of Fig. 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), in particular around 100–200 Hz. 3 the curve from Olive and Welti (2015) was lowered by 6.5 dB to facilitate easier comparison between the two. The two target curves are shown in the top panel of Fig. Two target curves were used: the overall mean curve across all measured headphones, and the target curve suggested by Olive and Welti (2015). 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. All headphones were categorized according to retail price quantile (with cutoffs of 25% and 75%) and headphone type (CA, SA, and IE). Headphone retail prices were determined using Google shopping in Australia and converted to USD using a fixed exchange rate of 0.75. In total, measurements for 283 headphones were acquired.

After interpolation, the mean response across frequency was subtracted. 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 using linear interpolation. The 10+ measurements were averaged in the spectral power density domain using a discrete Fourier transform. The responses for both the left and right channel were measured with a sampling rate of 48 kHz.

Target bookends verification#

Log sine sweeps rather than linear sine sweeps were employed to allow verification that non-linear distortion components were virtually absent. 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. Each headphone was reseated at least five times. 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). 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 Olive et al., 2013). The preferred response however seems to be listener, content, and headphone dependent ( Olive and Welti, 2015 Olive et al., 2016). In particular, research suggests that the frequency (magnitude) response is a major factor in listener preference scores ( Olive and Welti, 2012 Fleischmann et al., 2012 Olive et al., 2013), and one headphone can effectively be transformed into another one by means of headphone equalization ( Welti et al., 2016). Temme et al., 2014 Fleischmann et al., 2014). Work by various authors has indicated that the subjective quality is mostly correlated with linear (spectral) attributes instead of non-linear (distortion) metrics (cf. Objective assessments and subjective metrics for sound quality on headphones have been a subject of research, in particular over the last 5 years.