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Machine Learning: Predicting Audio Quality for high SPL Smartphone Recordings

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In this paper, we explore a machine learning approach to evaluate audio quality for high sound pressure level (SPL) smartphone recordings. Our study is based on perceptual evaluations conducted by technical experts on eight audio sub-attributes (tonal balance, treble, midrange, bass, dynamics, temporal artifacts, spectral artifacts, and other artifacts) of audio quality for 121 smartphones released from 2019 to 2021. To address this task, we propose a Convolutional Neural Network (CNN) model, which proves to be a simple yet effective choice. We employ a pre-augmentation technique to enhance the training dataset size, creating a comprehensive dataset comprising recording spectrograms and corresponding perceptual evaluation scores. Our findings indicate that while the CNN model has certain limitations, it demonstrates promising capabilities in predicting evaluation scores, particularly in aspects of tonal balance, bass, and spectral artifact assessment.

Express Paper 130; AES Convention 155; October 2023
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