Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

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.

Comparative Analysis of Different Sampling Rates on Environmental Sound Classification Using the Urbansound8k Dataset

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.

Intelligent Sound-Based Early Fault Detection System for Vehicles

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.

Recognition of sick pig cough sounds based on convolutional neural network in field situations

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.

A new fusion feature based on convolutional neural network for pig cough recognition in field situations

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.