Personalized Audio Quality Preference Prediction

Personalized Audio Quality Preference Prediction

This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare the inputs and predict the preference. Several different structures for each side of the...

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Veröffentlicht in:arXiv.org 2023-02
Hauptverfasser: Chung-Che, Wang, Yu-Chun, Lin, Yu-Teng, Hsu, Jang, Jyh-Shing Roger
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Sprache:eng
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Audio data
Coders
Customization
Earphones
Multilayer perceptrons
Multilayers
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Yu-Chun, Lin
Yu-Teng, Hsu
Jang, Jyh-Shing Roger
description This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare the inputs and predict the preference. Several different structures for each side of the siamese network are investigated, and an LDNet with PANNs' CNN6 as the encoder and a multi-layer perceptron block as the decoder outperforms a baseline model using only audio input the most, where the overall accuracy grows from 77.56% to 78.04%. Experimental results also show that using all the subject information, including age, gender, and the specifications of headphones or earphones, is more effective than using only a part of them.
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subjects Audio data
Coders
Customization
Earphones
Multilayer perceptrons
Multilayers
title Personalized Audio Quality Preference Prediction
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