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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.
Author (s): Guelen, Philippe; Zhao, Dan; Terra Pizutti Dos Santos, Pietro;
Drouadene, Arthur; Bacle, Justin
Affiliation:
DXOMARK; DXOMARK; DXOMARK; DXOMARK
(See document for exact affiliation information.)
AES Convention: 155
Paper Number:130
Publication Date:
2023-10-06
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Session subject:
Perception
Permalink: https://aes2.org/publications/elibrary-page/?id=22284
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Guelen, Philippe; Zhao, Dan; Terra Pizutti Dos Santos, Pietro; Drouadene, Arthur; Bacle, Justin; 2023; Machine Learning: Predicting Audio Quality for high SPL Smartphone Recordings [PDF]; DXOMARK; DXOMARK; DXOMARK; DXOMARK; Paper 130; Available from: https://aes2.org/publications/elibrary-page/?id=22284
Guelen, Philippe; Zhao, Dan; Terra Pizutti Dos Santos, Pietro; Drouadene, Arthur; Bacle, Justin; Machine Learning: Predicting Audio Quality for high SPL Smartphone Recordings [PDF]; DXOMARK; DXOMARK; DXOMARK; DXOMARK; Paper 130; 2023 Available: https://aes2.org/publications/elibrary-page/?id=22284
@article{guelen2023machine,
author={guelen philippe and zhao dan and terra pizutti dos santos pietro;
drouadene arthur and bacle justin},
journal={journal of the audio engineering society},
title={machine learning: predicting audio quality for high spl smartphone recordings},
year={2023},
number={130},
month={october},}