The trash in the ocean is causing harm to the marine environment. The current most used removal technique is the use of trawlers. It is a highly laborious job and requires high costs as well. With the help of Autonomous Underwater Vehicles (AUVs), removing marine debris could be one of the best and cheapest solutions available. This paper evaluates the use of the You Only Live Once Version 5 Small (YOLOv5s) to compare with the other networks used to identify marine debris. Without fine-tuning the YOLOv5s model in this study, it can achieve a Mean Average Precision (mAP) of 0.681 and an inference speed of 153.9 frames per second. It shows an improvement in mAP compared to YOLOv2, Tiny-YOLO, and Single Shot Multibox Detector.

The paper presents a wavelet scattering feature extraction using an averaged data augmentation to include unseen devices in training. The multiple classifiers are applied to the extracted features. The outputs from Gated Recurrent Units (GRUs), and the ensemble classifiers are maximally fused to classify sound coming from different devices and scenes. The average device-wise accuracy has more than 5.4% improvement with the proposed GRUs network than its counterpart Long short-term memory (LSTM) in device-wise classification. The device-wise classification accuracy for the proposed max-fusion exhibits approximately 19.1% better than the baseline results in DCASE2020-Task1A. The proposed max-fusion also demonstrates around 22.9% higher classification accuracy in scene-wise classification. Lastly, the proposed max-fusion method produces comparative results with the Snapshot ensembles that won the DCASE2020 Challenges-Task 1A.

This paper presents an automated environmental monitoring and control system for mushroom vertical farming in a refurbished shipping container. We describe the architecture of our NVIDIA Jetson Nano based system and presents the outcome of our successful attempts at cultivating Oyster mushrooms. The most important environmental parameters for healthy Oyster mushroom growth is temperature and humidity. We discuss the issues and challenges faced in this work, and suggest ways to improve on the current system in order to cultivate high-value mushrooms.

In this paper, a random sub-space discriminant classifier for classifying acoustic devices that combines the features obtained from Mel-frequency cepstral coefficients (MFCCs), and scalogram coefficients is proposed. The aggregated features for the random ensemble sub-space discriminant classifier model are used. The maximum weight fusion mechanisms are used to fuse the ensemble classifier’s results from the two sets of coefficients. With a higher concentration of scalogram coefficients, the accuracy improves by around 8.15% compared to single feature extraction via MFCCs. The random subspace discriminant classifier achieves the classification accuracy of approximately 74.9% or 20.8% better than the baseline result of 54.1% obtained in the DCASE2020-Task1A Challenge.

In this paper, we propose addressing the lack of strongly labeled data by using pseudo strongly labeled data approximated using Convolutive Nonnegative Matrix Factorization. Using this set of data, we then train a novel architecture called the Convolutional Macaron Net (CMN), which combines Convolutional Neural Network (CNN) with MN, in a semi-supervised manner. Instead of training only a single model or using the Mean-teacher approach, we train two different CMNs synchronously using a curriculum consistency cost and a curriculum interpolated consistency cost. In the inference stage, one of the models will provide the frame-level prediction while the other model will provide the clip-level prediction. Our system outperforms the baseline system of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4 by a margin of over 10% based on our proposed framework. By comparing with the top submission of the DCASE 2019 challenge, our system accuracy is also higher by 1.8%. On the other hand, as compared to the top submission of DCASE 2020, our accuracy is also marginally higher by 0.3%, even with fewer Transformer encoding layers. Our system remains robust on unseen YouTube evaluation dataset and has a winning margin of 0.6% and 6.3% against the top submission of DCASE 2019 and the baseline system.

This paper investigates how time scale affects the classification accuracy of log Mel-frequency coefficients and deep scattering spectrum for acoustic scene classification. Currently, log Mel-frequency coefficients has dominated in most acoustic classification task as observed in DCASE challenge. However, log Mel-frequency coefficients have two flaws; the first flaw is the Heisenberg uncertain property of short-time Fourier transform, which is caused by a fixed window size. A trade-off between having high frequency resolution while suffering from poor time resolution and vice versa. The next flaw occurs when applying mel-filter banks along frequency axis, resulting in a loss of information when the time scale is more than 25ms. To overcome this limitation, this paper explored deep scattering spectrum with various window intervals. Following the current framework of log Mel-frequency coefficients integration with convolution neural network, we proposed a two-stage convolution neural network model approach. The two-stage model is designed to tackle the huge disparity in magnitude of the deep scattering spectrum's first and second order coefficients. Next, we explored various feature normalization technique and applied on the input representation directly, thus allowing learning to occur. Lastly, our experimentation uses the DCASE 2020 Task 1a dataset, consisting of acoustic recordings from various environments or scenes and demonstrated that DSS has a slight advantage against MFSC and scored 70.36% and 69.42%, respectively.

Abstract Noise prediction is important for crew comfort in an offshore platform such as oil drilling rig. A deep neural network learning on the oil drilling rig is not widely studied. In this paper, a deep belief network (DBN) with the last layer initialized with trained DBN (named DBN-DNN) is used to model the sound pressure level (SPL) in the compartments of the oil drilling rig. The method finds an optimal number of the hidden neurons in restricted Boltzmann machine by using a normalized Euclidean distance from the worst possible error for each hidden layer progressively. The dataset used for experimental results is obtained via vibroacoustics simulation software such as VA-One and actual site measurements. The results show that output parameters such as spatial SPL, average spatial SPL, structure-borne SPL and airborne SPL improve the testing root mean square error to around 20% as compared to randomly assigning the number of neurons for each hidden layer. The testing RMSE in the output parameters has improved when compared with a multi-layer perceptron, sparse autoencoder, Softmax, self-taught learning and extreme learning machine.

Sound Event Detection remains a challenging task due to the lack of strongly labeled data. While the use of weakly labeled and unlabeled data can alleviate this issue, most states of the art utilized the Mean Teacher approach, which requires training two identical models in a semi-supervised manner. Such methodology can have two critical limitations. Firstly, it can be computationally expensive if a very deep model is designed. Secondly, a model designed might only be optimal for either audio tagging or frame-level prediction but not both. Thus, using the Mean-Teacher approach may only allow a model to perform at its maximum potential for one of the tasks. However, the aforementioned issues can be circumvented by designing two different models where the less complex model provides the frame-level prediction while the more complex model provides the audio tags. To increase the accuracy of the models, we propose the use of Squeeze and Excite, meta-ACON, an improved Transformer encoding layer, a triple instance-level pooling approach (i.e., multi-branch pooling), and an improved cyclic learning scheme. Based on such a framework, the best system can achieve an event-based F1-score of 48.5%. By ensembling the top 5 models, the event-based F1-score increases to 50.4%. The proposed framework can achieve a minimum margin of over 12% against the baseline system while being competitive to the other state of the arts.

Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue, distraction, drunkenness, and/or recklessness are the most common types of abnormal driving behavior that leads to an accident. With technological advances, modern smartphones have the capabilities for driving behavior analysis. There has not yet been a comprehensive review on methodologies utilizing only a smartphone for drowsiness detection and abnormal driver behavior detection. In this paper, different methodologies proposed by different authors are discussed. It includes the sensing schemes, detection algorithms, and their corresponding accuracy and limitations. Challenges and possible solutions such as integration of the smartphone behavior classification system with the concept of context-aware, mobile crowdsensing, and active steering control are analyzed. The issue of model training and updating on the smartphone and cloud environment is also included.

A modified multiple generalized regression neural network (GRNN) is proposed to predict the noise level of various compartments onboard of the offshore platform. With limited samples available during the initial design stage, GRNN can cause errors when it maps the available inputs to sound pressure level for the entire offshore platform. To obtain more relevant group for GRNNs training, fuzzy C-mean (FCM) is used. However, outliers in some group may interfere the prediction accuracy. The problem of selecting suitable inputs parameters (in each cluster) is often impeded by lack of accurate information. Principal component analysis (PCA) is used to ensure high relevance input variables in each cluster. By fusing multiple GRNNs by an optimal spread parameter, the proposed modeling scheme becomes quite effective for modeling multiple frequency-dependent data set (ranging from 125 to 8000 Hz) with different input parameters. The performance of FCM-PCA-GRNNs has improved significantly as the results show a 25% improvement on the spatial sound pressure level (SPL) and 85% improvement on the spatial average SPL than just GRNNs alone. By comparing with data obtained from real engine room on a jack-up rig, the FCM-PCA-GRNNs noise model performs better with around 16% less error than the empirical-based acoustic models. Additionally, the results show comparable performance to statistical energy analysis that requires more time and resources to solve during the early stage of the offshore platform design.