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The audio detection system enables autonomous cars to recognize their surroundings based on the noise produced by moving vehicles. This paper proposes the utilization of a machine learning model based on convolutional neural networks (CNN) integrated into an embedded system supported by a microphone. The system includes a specialized microphone and a main processor. The microphone enables the transmission of an accurate analog signal to the main processor, which then analyzes the recorded signal and provides a prediction in return. While designing an adequate hardware system is a crucial task that directly impacts the predictive capability of the system, it is equally imperative to train a CNN model with high accuracy. To achieve this goal, a dataset containing over 3000 up-to-5-second WAV files for four classes was obtained from open-source research. The dataset is then divided into training, validation, and testing sets. The training data is converted into images using the spectrogram technique before training the CNN. Finally, the generated model is tested on the testing segment, resulting in a model accuracy of 77.54%. [ABSTRACT FROM AUTHOR]

The temporal and spectral structure is possessed in the time-frequency domain by sound events. Analyzing and classifying acoustic environment using sound recording is an emerging research area. Convolutional layers can quickly extract high-level features and shift-invariant features from the time-frequency domain. In this work, emergency vehicle detection (EVD) like fire brigades, ambulances, and police cars is done based upon their siren sounds. Dataset from Google Audioset ontology was collected and features are extracted by Mel-frequency Cepstral Coefficient (MFCC). Three deep neural networks (DNN) models (dense layer, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)) with different configurations and parameters have been investigated. Then, an ensemble model has been designed with optimum selected models by performing experimental tests on various configurations with hyper-parameter tuning. The proposed ensemble model provides the highest accuracy of 98.7%, while the recurrent neural network (RNN) model provides an accuracy of 94.5%. Also, performance analysis of deep learning models is done with various machine learning models like Perceptron, SVM, decision tree etc. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (drones) are widely available on today's market for use for use both as a hobby and to meet specific industrial needs, such as agriculture or parcel transport. This intensive use in various areas has also led to an increase in the illegitimate and criminal use of them, which has led to an interest in identification and detection techniques in the academic world. To address this topic, we propose a method that automates drone detection processes using their acoustic characteristics with different machine learning algorithms. In this study, we show the advantage of using machine learning techniques for drone detection. Experimental analysis demonstrates that the proposed method is promising in drone detection. [ABSTRACT FROM AUTHOR]

Depression is a prevalent mental health disorder. The absence of specific biomarkers makes clinical diagnosis highly subjective. This makes it difficult to make a definitive diagnosis for the patient. Recently, deep learning methods have shown promise for depression detection. However, current methods tend to focus solely on the connections within or between audio signals, leading to limitations in the model's ability to recognize depression-related cues in audio signals and affecting its classification performance. To address these limitations, we propose a graph neural network approach for depression recognition that incorporates potential connections within and between audio signals. Specifically, we first use a gated recurrent unit (GRU) to extract time-series information between frame-level features of audio signals. We then construct two graph neural network modules sequentially to explore the potential connections within and between audio signals. The first graph network module constructs a graph using the frame-level features of each audio sample as nodes. The output is obtained as a graph-embedded feature vector representation after the graph convolution layers. Subsequently, the output graph embedding feature vector representation of the first graph network model is used as the nodes of the graph to construct the second graph network. The internal relationship between audio signals is encoded by the property of node neighborhood information propagation. In addition, we use a pre-trained emotion recognition network to extract emotional features that are highly correlated with depression. By further strengthening the connection weights among nodes in the second graph network through a self-attention mechanism, relevant cues are provided for the model to complete depression detection from audio signals. We conducted extensive experiments on three depression datasets, including DAIC-WOZ, MODMA, and D-Vlog. The proposed model achieves better results on several performance evaluation metrics such as accuracy, F1-score, precision, and recall compared to all the compared algorithms, validating its effectiveness. • We introduce a novel GNN model for detecting depression from audio signals. • Our model explores the inter-class variability and intra-class consistency of audios. • We use the self-attention mechanism fuses depression-related emotional features. • The proposed model achieves better results on three depression datasets. [ABSTRACT FROM AUTHOR]

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There are several problems such as high time consumption and low recognition accuracy on the short duration audio events. In this paper, an audio event recognition method that combined GMM and SVM was put forward. The method used the statistical distribution description of GMM and the promote generalization ability of SVM, and used the respective result of the recognition of GMM and SVM fusion processing, ten-type gunshots, such as handguns, rifles, machine guns etc. were used as the experimental data. Compared with the ordinary methods, which have to train 10 specific templates from each gunshot type, the proposed method just needed 2 templates to fulfill the recognition task. Experimental results show that the proposed method yields an accuracy of 92. 71%. Furthermore, because much fewer templates and training processes are required, this method is easy to implement and improves the efficiency significantly with a low algorithm complexity. [ABSTRACT FROM AUTHOR]

Characteristics extraction has a great effect on the audio training and recognition in the audio recognition system. MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate. For the situation that MFCC has a large amount of computation, an improved algorithm MFCC_E is introduced. The computation of MFCC_E is reduced by 50% compared with the standard algorithm MFCC, and it make the hardware implementation is easy. The experimental result indicated that MFCC_E and MFCC have the same recognition rate roughly, yet the computational complexity of MFCC_E is much smaller. [ABSTRACT FROM AUTHOR]

By analyzing the features of the audio signal, and solving the problem of confused recognition between pure music and speech sounds-music mixed part, a method which can recognize pure music and speech sounds-music mixed part, based on the average short time energy and standard deviation of zero-crossing rate features is put forward. It can precisely recognize the pure music and speech sounds-music mixed part, providing a method to pre-process the audio signal to get rid of the unnecessary part (meaningless part) of the audio signal, so that it can prove the efficiency and performance of the audio data feature extraction. By processing lots of different style, different singers and different languages, from the experimental results, the average correct recognition rate of the pure music part reached 92.30%, the average correct recognition rate of speech sounds-music mixed part reached 96.36%. [ABSTRACT FROM AUTHOR]