How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses
IEEE/ACM Transactions on Audio, Speech, and Language Processing(2024)
摘要
Advanced auditory models are useful in designing signal-processing algorithms
for hearing-loss compensation or speech enhancement. Such auditory models
provide rich and detailed descriptions of the auditory pathway, and might allow
for individualization of signal-processing strategies, based on physiological
measurements. However, these auditory models are often computationally
demanding, requiring significant time to compute. To address this issue,
previous studies have explored the use of deep neural networks to emulate
auditory models and reduce inference time. While these deep neural networks
offer impressive efficiency gains in terms of computational time, they may
suffer from uneven emulation performance as a function of auditory-model
frequency-channels and input sound pressure level, making them unsuitable for
many tasks. In this study, we demonstrate that the conventional
machine-learning optimization objective used in existing state-of-the-art
methods is the primary source of this limitation. Specifically, the
optimization objective fails to account for the frequency- and
level-dependencies of the auditory model, caused by a large input dynamic range
and different types of hearing losses emulated by the auditory model. To
overcome this limitation, we propose a new optimization objective that
explicitly embeds the frequency- and level-dependencies of the auditory model.
Our results show that this new optimization objective significantly improves
the emulation performance of deep neural networks across relevant input sound
levels and auditory-model frequency channels, without increasing the
computational load during inference. Addressing these limitations is essential
for advancing the application of auditory models in signal-processing tasks,
ensuring their efficacy in diverse scenarios.
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关键词
computational auditory modelling,deep learning,optimization
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