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

Hajihashemi, Vahid; Gharahbagh, Abdorreza Alavi; Cruz, Pedro; Ferreira, Marta Campos; Machado, J.J.M.; Tavares, João Manuel R. S.

doi:10.3390/s22041535

Cited by 10 publications

(5 citation statements)

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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%

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%

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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show abstract

“…[42]) or its tagging (e.g. [23,25,32,38,39,41]) but not simultaneously for both purposes. In this study, to the best of our knowledge, this is the first time that wavelets are being used both for the rare event detection and its classification, for the events occurring in the open environment.…”

Section: Contribution Of This Papermentioning

confidence: 99%

“…In [ 40 ], scalograms are used for extracting the sound features to be processed by a cascade network comprising a two-dimensional pre-trained CNN model and gated recurrent neural networks (GRNNs) with a highway network layer and a softmax layer for ASC. In [ 41 ], ASC is carried out by an ensemble classifier (consisting of two random sub-space classifiers), trained on the features extracted from audio signals by WSN. The outputs of the two classifiers are combined by using the mathematical formula whose parameters are determined by a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

DEW: A wavelet approach of rare sound event detection

Gul,

Khan,

Ur-Rehman

2024

PLoS ONE

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This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called ‘chunks’ are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

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“…An ensemble classifier-based technique for ASC was presented by [ 27 ]. First, the signal was separated into the left and right mono channels, and then feature extraction was carried out by applying wavelet scattering individually to the left and right channels.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The parameters of this step were selected using a genetic algorithm. This [ 27 ] technique has a greater classification accuracy of 95% on the Dcase2017 dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Siddiqui

Saleem

Raza

et al. 2022

Sensors

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Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.

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

Cited by 10 publications

References 50 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

DEW: A wavelet approach of rare sound event detection

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

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