Review of Standard Sound Quality Assessment Methods for the Transmitted and Processed Sounds

Oh, Wongeun

doi:10.7776/ask.2013.32.3.214

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…3 (d). The motion sound was saved in a wave file using a 44.1-kHz sampling rate and converted into a Mel-spectrogram image [18]…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Flow-Based Anomaly Detection Model for Motor Gearbox Defect Detection

2023

KSII TIIS

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In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.

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“…3 (d). The motion sound was saved in a wave file using a 44.1-kHz sampling rate and converted into a Mel-spectrogram image [18]…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Flow-Based Anomaly Detection Model for Motor Gearbox Defect Detection

2023

KSII TIIS

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“…The use of audio/acoustic modality for depression detection involves changing audio data into specific forms. The most widely known forms of speech data are the Mel Frequency Cepstrum Coefficient (MFCC) [9], Log Mel spectrograms, Mel spectrograms, and COVAREP features [10].…”

Section: A Audio Modalitymentioning

confidence: 99%

Additive Cross-Modal Attention Network (ACMA) for Depression Detection Based on Audio and Textual Features

Iyortsuun,

Kim,

Yang

et al. 2024

IEEE Access

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Detecting depression involves using standardized questionnaires like the Patient Health Questionnaires (PHQ-8/9). Yet, patients might not always provide genuine responses, leading to potential misdiagnoses. Therefore, the need for a means to detect depression in patients without the use of preset questions is of high importance. Addressing this challenge, our study aims to discern telltale symptoms from statements made by the patient. We harness both audio and text data, proposing an Additive cross-modal attention network to learn and pick up the appropriate weights that best capture the cross-modal interactions and relationships between both features using BiLSTM as the backbone of both modalities. We tested our approach on the DAIC-WOZ dataset for depression detection and also evaluated our model performance on the EATD-Corpus. Benchmarked against similar studies on this dataset, our method demonstrates commendable efficacy in both classification and regression models for both unimodal and multimodal approaches. Our findings underscore the potential of our model to effectively detect depression in patients while using textual and speech modalities without the necessary use of preset questions for effective detection.

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Review of Standard Sound Quality Assessment Methods for the Transmitted and Processed Sounds

Cited by 2 publications

References 17 publications

Hierarchical Flow-Based Anomaly Detection Model for Motor Gearbox Defect Detection

Hierarchical Flow-Based Anomaly Detection Model for Motor Gearbox Defect Detection

Additive Cross-Modal Attention Network (ACMA) for Depression Detection Based on Audio and Textual Features

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