Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different...Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and complex.This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources.In this research,the performance of transformer and convolutional neural networks(CNN)are investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,databases.Moreover,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio clips.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database.For UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 respectively.The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.展开更多
Environmental sound classification (ESC) has gained increasing attention in recent years. This study focuses on the evaluation of the popular public dataset Urbansound8k (Us8k) at different sampling rates using hand c...Environmental sound classification (ESC) has gained increasing attention in recent years. This study focuses on the evaluation of the popular public dataset Urbansound8k (Us8k) at different sampling rates using hand crafted features. The Us8k dataset contains environment sounds recorded at various sampling rates, and previous ESC works have uniformly resampled the dataset. Some previous work converted this data to different sampling rates for various reasons. Some of them chose to convert the rest of the dataset to 44,100, as the majority of the Us8k files were already at that sampling rate. On the other hand, some researchers down sampled the dataset to 8000, as it reduced computational complexity, while others resampled it to 16,000, aiming to achieve a balance between higher classification accuracy and lower computational complexity. In this research, we assessed the performance of ESC tasks using sampling rates of 8000 Hz, 16,000 Hz, and 44,100 Hz by extracting the hand crafted features Mel frequency cepstral coefficient (MFCC), gamma tone cepstral coefficients (GTCC), and Mel Spectrogram (MelSpec). The results indicated that there was no significant difference in the classification accuracy among the three tested sampling rates.展开更多
An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the...An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.展开更多
Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem.For accurate audio signal classification,suitable and efficient techniques are needed,particularly mac...Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem.For accurate audio signal classification,suitable and efficient techniques are needed,particularly machine learning approaches for automated classification.Due to the dynamic and diverse representative characteristics of audio data,the probability of achieving high classification accuracy is relatively low and requires further research efforts.This study proposes an ensemble model based on the LeNet and hierarchical attention mechanism(HAM)models with MFCC features to enhance the models’capacity to handle bias.Additionally,CNNs,bidirectional LSTM(BiLSTM),CRNN,LSTM,capsule network model(CNM),attention mechanism(AM),gated recurrent unit(GRU),ResNet,EfficientNet,and HAM models are implemented for performance comparison.Experiments involving the DCASE2020 dataset reveal that the proposed approach works better than the others,achieving an impressive 99.13%accuracy and 99.56%k-fold cross-validation accuracy.Comparison with state-of-the-art studies further validates this performance.The study’s findings highlight the potential of the proposed approach for accurate fault detection in vehicles,particularly involving the use of acoustic data.展开更多
Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,...Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.展开更多
Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease ea...Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection.Owing to complicated interferences in piggery,recognition of pig cough sound becomes difficult.Although a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs enhancement.The purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm system.We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectro-gram.With the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the accu-racy.We compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing algorithms.The results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.展开更多
Pig cough is considered the most common clinical symptom of respiratory diseases.Thus,establishing an early warning system for respiratory diseases in pigs by monitoring and identifying their cough sounds is important...Pig cough is considered the most common clinical symptom of respiratory diseases.Thus,establishing an early warning system for respiratory diseases in pigs by monitoring and identifying their cough sounds is important.In this paper,we propose a new fusion feature,namely Mel-frequency cepstral coefficient-convolutional neural network(MFCC-CNN),to improve the recognition accuracy of pig coughs.We obtained the MFCC-CNN feature by fusing multiple frames of MFCC with multiple one-layer CNNs.We used softmax and linear support vector machine(SVM)classifiers for classification.We tested the algorithm through field experiments.The results reveal that the performance of classifiers using the MFCC-CNN feature was significantly better than those using the MFCC feature.The F1-score increased by 10.37%and 5.21%,and the cough accuracy increased by 7.21%and 3.86%for the softmax and SVM classifiers,respectively.We also analyzed the impact of different numbers of fusion frames on the classification performance.The results reveal that fusing 55 and 45 adjacent frames resulted in the best performance for the softmax and SVM classifiers,respectively.From this research,we can conclude that a system constructed by simple one-layer CNNs and SVM classifiers can demonstrate excellent performance in pig sound recognition.展开更多
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabia.
文摘Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and complex.This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources.In this research,the performance of transformer and convolutional neural networks(CNN)are investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,databases.Moreover,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio clips.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database.For UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 respectively.The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.
文摘Environmental sound classification (ESC) has gained increasing attention in recent years. This study focuses on the evaluation of the popular public dataset Urbansound8k (Us8k) at different sampling rates using hand crafted features. The Us8k dataset contains environment sounds recorded at various sampling rates, and previous ESC works have uniformly resampled the dataset. Some previous work converted this data to different sampling rates for various reasons. Some of them chose to convert the rest of the dataset to 44,100, as the majority of the Us8k files were already at that sampling rate. On the other hand, some researchers down sampled the dataset to 8000, as it reduced computational complexity, while others resampled it to 16,000, aiming to achieve a balance between higher classification accuracy and lower computational complexity. In this research, we assessed the performance of ESC tasks using sampling rates of 8000 Hz, 16,000 Hz, and 44,100 Hz by extracting the hand crafted features Mel frequency cepstral coefficient (MFCC), gamma tone cepstral coefficients (GTCC), and Mel Spectrogram (MelSpec). The results indicated that there was no significant difference in the classification accuracy among the three tested sampling rates.
基金The authors are pleased to announce that The Superior University,Lahore,sponsors this research.
文摘An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R746),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem.For accurate audio signal classification,suitable and efficient techniques are needed,particularly machine learning approaches for automated classification.Due to the dynamic and diverse representative characteristics of audio data,the probability of achieving high classification accuracy is relatively low and requires further research efforts.This study proposes an ensemble model based on the LeNet and hierarchical attention mechanism(HAM)models with MFCC features to enhance the models’capacity to handle bias.Additionally,CNNs,bidirectional LSTM(BiLSTM),CRNN,LSTM,capsule network model(CNM),attention mechanism(AM),gated recurrent unit(GRU),ResNet,EfficientNet,and HAM models are implemented for performance comparison.Experiments involving the DCASE2020 dataset reveal that the proposed approach works better than the others,achieving an impressive 99.13%accuracy and 99.56%k-fold cross-validation accuracy.Comparison with state-of-the-art studies further validates this performance.The study’s findings highlight the potential of the proposed approach for accurate fault detection in vehicles,particularly involving the use of acoustic data.
基金funded by Climate Change AI(2023 innovation grant-https://www.climatechange.ai/innovation_grants).
文摘Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.
基金This work was supported by the grant from the National Key Research and Development Program of China under Grant 2016YFD0700204-02the Earmarked Fund for China Agricul-ture Research System under Grant CARS-35+2 种基金the"Young Talents"Project of Northeast Agricultural University under Grant 17QC20the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2020092 and UNPYSCT-2018142and the Hei-longjiang Post-doctoral Subsidy Project of China under Grant LBH-Z17020.
文摘Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection.Owing to complicated interferences in piggery,recognition of pig cough sound becomes difficult.Although a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs enhancement.The purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm system.We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectro-gram.With the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the accu-racy.We compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing algorithms.The results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.
基金supported by the grant from the National Key Research and Development Program of China under Grant 2016YFD0700204-02the Earmarked Fund for China Agriculture Research System under Grant CARS-35+2 种基金the“Young Talents”Project of Northeast Agricultural University under Grant 17QC20the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2020092 and UNPYSCT-2018142the Heilongjiang Post-doctoral Subsidy Project of China under Grant LBH-Z17020.
文摘Pig cough is considered the most common clinical symptom of respiratory diseases.Thus,establishing an early warning system for respiratory diseases in pigs by monitoring and identifying their cough sounds is important.In this paper,we propose a new fusion feature,namely Mel-frequency cepstral coefficient-convolutional neural network(MFCC-CNN),to improve the recognition accuracy of pig coughs.We obtained the MFCC-CNN feature by fusing multiple frames of MFCC with multiple one-layer CNNs.We used softmax and linear support vector machine(SVM)classifiers for classification.We tested the algorithm through field experiments.The results reveal that the performance of classifiers using the MFCC-CNN feature was significantly better than those using the MFCC feature.The F1-score increased by 10.37%and 5.21%,and the cough accuracy increased by 7.21%and 3.86%for the softmax and SVM classifiers,respectively.We also analyzed the impact of different numbers of fusion frames on the classification performance.The results reveal that fusing 55 and 45 adjacent frames resulted in the best performance for the softmax and SVM classifiers,respectively.From this research,we can conclude that a system constructed by simple one-layer CNNs and SVM classifiers can demonstrate excellent performance in pig sound recognition.
