Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments

Hajihashemi, Vahid; Gharahbagh, Abdorreza Alavi; Oliveira, Hugo S.; Cruz, Pedro; Tavares, João Manuel R. S.

doi:10.1007/978-3-030-93420-0_38

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…In recent years, multiresolution analyses, such as spectrograms, mel frequency cepstral coefficients (MFCCs), and wavelets, have been widely used in signal analysis and AED because of their suitability for finding patterns in time-varying signals. Hajihashemi et al [1,2] used MFCC and wavelets for sound analysis in AED and acoustic scene classification. The authors also used wavelet scattering as another spectral feature in [1].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hajihashemi et al [1,2] used MFCC and wavelets for sound analysis in AED and acoustic scene classification. The authors also used wavelet scattering as another spectral feature in [1]. Roy et al [3] used the spectrogram as a time-frequency expression of arterial Doppler signals to predict blood clots and microemboli.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where S 2 1 and S 2 2 are the variances calculated from two populations and n 1 , n 2 are the number of samples as in Equation (1). If the variances are unequal, the following equation [73] can be used:…”

Section: Student's T-test Analysismentioning

confidence: 99%

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A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of frequency bands. A big challenge is that some of the spectrogram bands may be similar in different events and be useless in AED. Removing useless bands reduces the input feature dimension and is highly desirable. This article proposes a mathematical feature analysis method to identify and eliminate ineffective spectrogram bands and improve AED systems’ efficiency. The proposed approach uses a Student’s t-test to compare frequency bands of the spectrogram from different acoustic events. The similarity between each frequency band among events is calculated using a two-sample t-test, allowing the identification of distinct and similar frequency bands. Removing these bands accelerates the training speed of the used classifier by reducing the number of features, and also enhances the system’s accuracy and efficiency. Based on the obtained results, the proposed method reduces the spectrogram bands by 26.3%. The results showed an average difference of 7.77% in the Jaccard, 4.07% in the Dice, and 5.7% in the Hamming distance between selected bands using train and test datasets. These small values underscore the validity of the obtained results for the test dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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“…Usually, STFT is not used individually and its features are combined with other features, such as Mel Frequency Cepstral Coefficients (MFCC), Mel-frequency cepstrum (MFC) and log-Mel spectrogram instead. MFCCs are coefficients of MFC that independently or in combination with STFT and wavelet can be used for audio processing [9]. Mel-frequency cepstrum is the logarithmic power spectrum of the linear cosine transform of short-term audio signals in a nonlinear scale of frequency, usually known as Mel.…”

Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion

Hajihashemi

Gharahbagh

Cruz³

et al. 2022

Sensors

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The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.

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“…The extraction of discriminative features is one of the main challenges in this area. Mell Frequency Spectrum Coefficients [3][4][5][6], Hilbert-Huang transform [7], S transform [8], Wavelet transform [9][10][11][12], and multi-domain features [13] are well-known feature extractors that have been proposed for PCG classification.…”

Section: Introductionmentioning

confidence: 99%

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning

Harimi

Majd

Gharahbagh

et al. 2022

Sensors

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Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning—among others—mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.

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Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments

Cited by 6 publications

References 33 publications

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning

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