A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer, Jakob

doi:10.3390/app10062020

Cited by 129 publications

(98 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Audio-based risky behavior detection is based on complex features and distinguishable behaviors (e.g., coughing, sneezing, background noise), which requires a deeper CNN model than shallow architecture (i.e., two or three-layer architecture) offers [ 75 ]. VGG16 has been adopted for audio event detection and demonstrated significant literature results [ 71 ]. The feature maps were flattened to obtain the fully connected layer after the last convolutional layer.…”

Section: Resultsmentioning

confidence: 99%

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Liang

Saeedi

Ojagh

et al. 2020

Sensors

View full text Add to dashboard Cite

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

show abstract

Section: Resultsmentioning

confidence: 99%

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Liang

Saeedi

Ojagh

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A recent trend in the field of ASC is to adopt data driven methods, wherein acoustic scene features are learned from data [ 34 ]. Among the convolutional neural network (CNN) models, the ResNet model [ 35 ] exhibits high accuracy and thus is typically used as a backbone neural network model [ 36 , 37 , 38 , 39 ].…”

Section: Related Workmentioning

confidence: 99%

Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Kim

Moon

et al. 2021

Sensors

View full text Add to dashboard Cite

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently utilizes unified memory for reducing the latency of data transfer during network model training. In addition, we propose the layer-wise processor selection method to consider the latency generated by the difference in the processor performing the forward propagation step and the backpropagation step in the same layer. The experiments were performed on an ODROID-XU4 with the ResNet-18 model, and the experimental results indicate that the proposed method reduces the latency by at most 28.4% compared to the central processing unit (CPU) and at most 21.8% compared to the graphics processing unit (GPU). Through experiments using various batch sizes to measure the average power consumption, we confirmed that device heterogeneity is alleviated by performing on-device learning using the proposed method.

show abstract

“…The task of determining the source of a sound is known as Sound Event Detection (SED). Although conventional machine learning algorithms have been used for SED in the past, current state-of-the-art approaches are based on deep learning models [7,8]. Over the last years, deep learning algorithms have consistently outperformed conventional machine learning ones in the annual Detection and Classification of Acoustic Scenes and Events (DCASE) challenge [7].…”

Section: Related Workmentioning

confidence: 99%

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Harana¹,

Alarcón²,

González³

et al. 2020

Preprint

View full text Add to dashboard Cite

For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective "Detect and Avoid" systems which enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based "Detect and Avoid" system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our aircraft sounds dataset and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with > 70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e. 10 Hz). While our proposed "Detect and Avoid" system already allows the detection of small aircraft from sound in real time, a larger dataset, sensor fusion, or the use of cloud-based services for remote computations could further improve its performance.

show abstract

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Cited by 129 publications

References 61 publications

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Contact Info

Product

Resources

About