Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognition on Embedded Systems

Silva, Bruno da; Happi, Axel W.; Braeken, An; Touhafi, Abdellah

doi:10.3390/app9183885

Cited by 30 publications

(25 citation statements)

References 21 publications

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“…These are networks of tiny and low-power autonomous nodes that are equipped with microphone-based sensing, processing, and communicating facilities. Such nodes are based on "embedded audio" platforms, i.e., embedded systems dedicated to digital audio processing (see, e.g., the Bela board [24]), where a variety of audio software runs on single board computers such as the Raspberry Pi or the Beaglebone [25].…”

Section: A Wireless Acoustic Sensors Networkmentioning

confidence: 99%

“…The recent availability of large amounts of recordings have fueled research on the use of machine learning methods to gather high level information about the sound environment, particularly in urban areas [25]. A scientific community emerged in 2010 to address this topic and the first Detection and Classification of Acoustic Scene and Events challenge was launched in 2013 [129], sponsored by the IEEE Acoustics, Speech, and Signal Processing Society.…”

Section: Machine Analysis Of Audio Contentmentioning

confidence: 99%

See 1 more Smart Citation

The Internet of Audio Things: State of the Art, Vision, and Challenges

Turchet

Fazekas

Lagrange

et al. 2020

IEEE Internet Things J.

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The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this paper, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.

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Section: A Wireless Acoustic Sensors Networkmentioning

confidence: 99%

Section: Machine Analysis Of Audio Contentmentioning

confidence: 99%

The Internet of Audio Things: State of the Art, Vision, and Challenges

Turchet

Fazekas

Lagrange

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Authors in Ref. [25] evaluated the performance of Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) to recognize urban sound on embedded devices concerning execution time and accuracy and they have also proposed a cascade approach to combine ML algorithms by analyzing the characteristics of embedded devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network

Ahmed¹,

Robin²,

Shafin³

2020

IJMECS

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Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.

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“…Moreover, they are in general battery powered, which limits the use of complex algorithms, which can drastically reduce the autonomy of the system. Although recent works have proposed solutions [7], these limitations have slowed the deployment of ESR on embedded devices.…”

Section: Introductionmentioning

confidence: 99%

MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification

et al. 2021

Self Cite

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Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time.

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Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognition on Embedded Systems

Cited by 30 publications

References 21 publications

The Internet of Audio Things: State of the Art, Vision, and Challenges

The Internet of Audio Things: State of the Art, Vision, and Challenges

Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network

MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification

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