Noise robust sound event classification with convolutional neural network

Özer, İlyas; Ozer, Zeynep; Fındık, Oğuz

doi:10.1016/j.neucom.2017.07.021

“…Next, the grayscale image is quantized into its RGB components; the mapping type is the

in MATLAB r2016b [ 38 ]. The mapping is expressed as

where

is the RGB spectrogram image and

is the non-linear

quantization function [ 32 ]. It is worth noting that, to facilitate the observation and analysis of the RGB spectrogram image, we deploy the color mapping in this paper.…”

Section: Signal Model and Time–frequency Analysis Methodsmentioning

confidence: 99%

“…First, the time–frequency analysis method based on the windowed short-time Fourier transform (STFT) [ 31 ] is employed to generate the spectrum of the MIMO-modulated signals. Then, the spectrum with different time windows is converted to a grayscale image, and this grayscale image is further transferred to the RGB spectrogram image [ 32 ]. Second, a fine-tuned AlexNet-based convolutional neural network (CNN) model is introduced to learn the features from the RGB spectrogram images.…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Jiang

¹

,

Wu

²

,

Wang

³

et al. 2021

Sensors

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…CNN is a typical multi-layer neural network first proposed for computer vision problems and it is very suitable for image-related applications in machine learning. In recent years, the application of CNN to environment sound classification has been widely reported [22][23][24][25]. We compute STFT spectrogram in each sound frame and then feed it as an image input to the CNN classifier.…”

Section: Classical Machine Learningmentioning

confidence: 99%

Sound-based Transportation Mode Recognition with Smartphones

Wang

¹

,

Roggen

²

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…Sound-event classification has seen a noticeable increase in interest as a field of research [1][2][3][4][5][6]. Supported by advancements in information communication technology (ICT) and convergence technology in the Industry 4.0 era [7], various industries are conducting research using sound data.…”

Section: Introductionmentioning

confidence: 99%

Noise-Robust Sound-Event Classification System with Texture Analysis

Choi

¹

,

Atif

²

,

Lee

³

et al. 2018

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

show abstract

Noise robust sound event classification with convolutional neural network

Cited by 88 publications

References 52 publications

Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sound-based Transportation Mode Recognition with Smartphones

Noise-Robust Sound-Event Classification System with Texture Analysis

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