Human auditory model based real-time smart home acoustic event monitoring

Mondal, Sujoy; Barman, Abhirup Das

doi:10.1007/s11042-021-11455-1

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…The Gammatone filter bank simulates the response of the cochlea to a sound signal. The formula for the time-domain impulse response of the Gammatone filter is shown in Equation (4) [30].…”

Section: Filtering and Spectrogram Fusionmentioning

confidence: 99%

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Yang,

Guan,

Yang

et al. 2023

Electronics

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The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet.

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“…The Gammatone filter bank simulates the response of the cochlea to a sound signal. The formula for the time-domain impulse response of the Gammatone filter is shown in Equation (4) [30].…”

Section: Filtering and Spectrogram Fusionmentioning

confidence: 99%

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Yang,

Guan,

Yang

et al. 2023

Electronics

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“…Human care robots and smart homes need to recognize sounds occurring in the users' private space in real time and at all times without any off time to provide services to the users. So, human care robots and smart homes store the incoming sound data in real time as audio files with a certain duration using microphones, and they send the audio files to the server for data pre-processing and event recognition [32][33][34][35]. This system enables the robots and the smart homes to recognize sound events in real time and automatically.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

Kim,

Nam,

Choi

et al. 2024

IEEE Access

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In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user's choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot's real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.INDEX TERMS Sound event recognition, human care robot, custom sound event, real-time system

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“…It has advantages such as lower cost, smaller bandwidth, storage, and computing requirements compared to video detection. In applications such as home environment [1,2], biometric monitoring, environmental sound event detection, indoor emergency monitoring [3][4][5], etc., indoor abnormal sound identification has won people's favor with its safe, low-cost, accurate and effective concepts [6].…”

Section: Introductionmentioning

confidence: 99%

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Song,

Xiong,

Wang

et al. 2024

Applied Sciences

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Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method’s potential in the identification of indoor abnormal sound events.

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Human auditory model based real-time smart home acoustic event monitoring

Cited by 10 publications

References 32 publications

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

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