Perceived Audio Quality of Sounds Degraded by Nonlinear Distortions and Single-Ended Assessment Using HASQI

Abstract

For field recordings and user generated content recorded on phones, tablets, and other mobile devices nonlinear distortions caused by clipping and limiting at pre-amplification stages, and dynamic range control (DRC) are common causes of poor audio quality. A single-ended method to detect these distortions and predict perceived degradation in speech, music, and soundscapes has been developed. This was done by training an ensemble of decision trees. During training, both clean and distorted audio was available and so the perceived quality could be gauged using HASQI (Hearing Aid Sound Quality Index). The new single-ended method can correctly predict HASQI from distorted samples to an accuracy of ±0.19 (95% confidence interval) using a quality range between 0.0 and 1.0. The method also has potential for estimating HASQI when other types of degradations are present. Subsequent perceptual tests validated the method for music and soundscapes. For the average mean opinion score for perceived audio quality on a scale from 0 to 1, the single ended method could estimate it within ±0.33.

Using Objective Metrics to Measure Hearing Aid Performance

“…Both metrics incorporate a model of the auditory periphery that includes auditory frequency analysis that depends on signal level and audiogram, auditory dynamic-range compression, the shift in auditory threshold that corresponds to the hearing loss, and neural firing-rate adaptation. The metrics have been validated for a wide range of hearing-aid processing algorithms and signal degradation conditions, including frequency-response shaping, wide dynamic-range compression (WDRC), noise suppression, feedback cancellation, frequency lowering, additive noise and babble, and nonlinear distortion such as amplitude quantization and peak clipping (Kates and Arehart, 2010; Houben et al ., 2011; Kressner et al ., 2013; Suelzle et al ., 2013; Kates and Arehart, 2014a; Kates and Arehart, 2014b; Huber et al ., 2014; Falk et al ., 2015; Kendrick et al ., 2015, Van Kuyk et al ., 2017).…”

Method to control the amount of “musical” noise for speech quality assessments

“…where x + (n) and x À (n) are mixed signals, s(n) is the speech signal and w(n) is the noise signal, all in the discrete time domain, i.e., as function of the sampled time n. We used Gaussian noise with a lower band limit of 20 Hz and an upper band limit of 8 kHz to mask speech sampled at 16 kHz. With equation (1) we calculated one estimated speech signal for each mixed signal of equation (2). By summing the two estimated speech signals, the residual noises cancel out and we obtain the clean speech signal with musical noise distortions.…”

“…Nonspeech audio and speech signal distortions appear in many communication systems, e.g., in telephones, headphones, and hearing aids. The causes of these distortions are manifold, e.g., bandwidth limitations, audio codecs, internal noise, dynamic range compression, or noise reduction artifacts [1][2][3][4]. Many psychoacoustical experiments rely on a controllable simulation of these distortions to examine human perception of distorted audio signals.…”

Speech quality and stable gain trade-offs in adaptive feedback cancellation for hearing aids

“…In addition, HASQI has been validated by several perceptual quality experiments. 3,[13][14][15] HASQI is therefore sensitive to changes in the speech spectrum introduced by acoustic feedback, whistling or ringing in the HA, and any nonlinear distortion introduced by the feedback-cancellation processing.…”