Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information

Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the si...
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Autor*in:	Weiyun Jin [verfasserIn] Xiaohua Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	spectral entropy-based magnitude feature with random forest classifier (SEM-RF) multi-time resolution magnitude and phase (multi-MP) feature linear discriminant analysis-random forest (LDA-RF) ImSEM-RF

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 13(2022), 2, p 79
Übergeordnetes Werk:	volume:13 ; year:2022 ; number:2, p 79

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info13020079

Katalog-ID:	DOAJ014357887

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callnumber-first	T - Technology
author	Weiyun Jin
spellingShingle	Weiyun Jin misc T58.5-58.64 misc spectral entropy-based magnitude feature with random forest classifier (SEM-RF) misc multi-time resolution magnitude and phase (multi-MP) feature misc linear discriminant analysis-random forest (LDA-RF) misc ImSEM-RF misc Information technology Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information
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topic_title	T58.5-58.64 Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information spectral entropy-based magnitude feature with random forest classifier (SEM-RF) multi-time resolution magnitude and phase (multi-MP) feature linear discriminant analysis-random forest (LDA-RF) ImSEM-RF
topic	misc T58.5-58.64 misc spectral entropy-based magnitude feature with random forest classifier (SEM-RF) misc multi-time resolution magnitude and phase (multi-MP) feature misc linear discriminant analysis-random forest (LDA-RF) misc ImSEM-RF misc Information technology
topic_unstemmed	misc T58.5-58.64 misc spectral entropy-based magnitude feature with random forest classifier (SEM-RF) misc multi-time resolution magnitude and phase (multi-MP) feature misc linear discriminant analysis-random forest (LDA-RF) misc ImSEM-RF misc Information technology
topic_browse	misc T58.5-58.64 misc spectral entropy-based magnitude feature with random forest classifier (SEM-RF) misc multi-time resolution magnitude and phase (multi-MP) feature misc linear discriminant analysis-random forest (LDA-RF) misc ImSEM-RF misc Information technology
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title	Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information
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title_full	Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information
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doi_str_mv	10.3390/info13020079
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title_sort	audio classification algorithm for hearing aids based on robust band entropy information
callnumber	T58.5-58.64
title_auth	Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information
abstract	Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the similarity and stability of band signals is introduced to improve the classification accuracy of each audio environment. The random forest (RF) model is applied to perform the classification process. Subsequently, to resolve the problem of decreasing classification accuracy of the SEM-RF algorithm in mixed speech environments, an improved algorithm, ImSEM-RF, is proposed. The SEM features and corresponding phase features are fused on multiple time resolutions to form a robust multi-time resolution magnitude and phase (multi-MP) feature, which improves the stability of the feature with which the speech signal interferes. The RF model is improved using the linear discriminant analysis (LDA) method to form a linear discriminant analysis-random forest (LDA-RF) joint classification model, which performs model acceleration. Through experiments on hearing aid research data sets for acoustic environment recognition, the effectiveness of the SEM-RF algorithm was confirmed on a background audio signal dataset. The classification accuracy increased by approximately 7% compared with the background noise classification algorithm using an RF tree classifier. The validity of the ImSEM-RF algorithm in speech-interference environments was confirmed using the speech in the background audio signal dataset. Compared with the SEM-RF algorithm, the classification accuracy was improved by approximately 2%. The LDA-RF reduced the program’s running time by <80% with multi-MP features compared with RF.
abstractGer	Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the similarity and stability of band signals is introduced to improve the classification accuracy of each audio environment. The random forest (RF) model is applied to perform the classification process. Subsequently, to resolve the problem of decreasing classification accuracy of the SEM-RF algorithm in mixed speech environments, an improved algorithm, ImSEM-RF, is proposed. The SEM features and corresponding phase features are fused on multiple time resolutions to form a robust multi-time resolution magnitude and phase (multi-MP) feature, which improves the stability of the feature with which the speech signal interferes. The RF model is improved using the linear discriminant analysis (LDA) method to form a linear discriminant analysis-random forest (LDA-RF) joint classification model, which performs model acceleration. Through experiments on hearing aid research data sets for acoustic environment recognition, the effectiveness of the SEM-RF algorithm was confirmed on a background audio signal dataset. The classification accuracy increased by approximately 7% compared with the background noise classification algorithm using an RF tree classifier. The validity of the ImSEM-RF algorithm in speech-interference environments was confirmed using the speech in the background audio signal dataset. Compared with the SEM-RF algorithm, the classification accuracy was improved by approximately 2%. The LDA-RF reduced the program’s running time by <80% with multi-MP features compared with RF.
abstract_unstemmed	Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the similarity and stability of band signals is introduced to improve the classification accuracy of each audio environment. The random forest (RF) model is applied to perform the classification process. Subsequently, to resolve the problem of decreasing classification accuracy of the SEM-RF algorithm in mixed speech environments, an improved algorithm, ImSEM-RF, is proposed. The SEM features and corresponding phase features are fused on multiple time resolutions to form a robust multi-time resolution magnitude and phase (multi-MP) feature, which improves the stability of the feature with which the speech signal interferes. The RF model is improved using the linear discriminant analysis (LDA) method to form a linear discriminant analysis-random forest (LDA-RF) joint classification model, which performs model acceleration. Through experiments on hearing aid research data sets for acoustic environment recognition, the effectiveness of the SEM-RF algorithm was confirmed on a background audio signal dataset. The classification accuracy increased by approximately 7% compared with the background noise classification algorithm using an RF tree classifier. The validity of the ImSEM-RF algorithm in speech-interference environments was confirmed using the speech in the background audio signal dataset. Compared with the SEM-RF algorithm, the classification accuracy was improved by approximately 2%. The LDA-RF reduced the program’s running time by <80% with multi-MP features compared with RF.
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container_issue	2, p 79
title_short	Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information
url	https://doi.org/10.3390/info13020079 https://doaj.org/article/2514e8b20a56400a93818f22c950015f https://www.mdpi.com/2078-2489/13/2/79 https://doaj.org/toc/2078-2489
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up_date	2024-07-03T22:37:37.930Z
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