Using Smartphones to Classify Urban Sounds

Gomes, Elsa Ferreira; Batista, Fabio; Jorge, Alípio Mário

doi:10.1145/2948992.2949002

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The authors of Reference [40] used Random Forests and SVM methods for the recognition of street music, siren, gun shot, idling, drilling, dog bark, children playing, car horn and air conditioner sounds. This study used MFCC and motif features, reporting an accuracy between 26.45% and 55.68% with SVM, and between 70.55% and 85% with Random Forests.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

et al. 2019

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The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.

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Section: Related Workmentioning

confidence: 99%

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

et al. 2019

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“…The authors of [38] used Random Forests and SVM methods for the recognition of air conditioner, car horn, children playing, dog bark, drilling, idling, gum shot, jackhammer, siren, and street music sounds, using MFCC and motif features, reporting an accuracy between 26.45% and 55.68% with SVM, and between 70.55% and 85% with Random Forests.…”

Section: Related Workmentioning

confidence: 99%

“…In [36], the authors used GMM with MFCC as features for the recognition of calls during driving, reporting an accuracy around 86%. On the other hand, the authors of [37] used GMM with zero crossing rate, RMS, MFCC, and low energy frame rate as features for the recognition of emotional states, reporting an accuracy between 65% and 100%.The authors of [38] used Random Forests and SVM methods for the recognition of air conditioner, car horn, children playing, dog bark, drilling, idling, gum shot, jackhammer, siren, and street music sounds, using MFCC and motif features, reporting an accuracy between 26.45% and 55.68% with SVM, and between 70.55% and 85% with Random Forests.In [39], the authors used the decision tree and HMM methods for the recognition of several ADL and environments, including zero crossing rate, low energy frame rate, spectral flux, spectral roll-off, bandwidth, normalized weighted phase deviation, and Relative Spectral Entropy (RSE), reporting an accuracy higher than 78%.The authors of [40] implemented the GMM, Feed-Forward DNN, Recurrent Neural Networks (RNN), and SVM for the recognition of baby crying and smoking alarm, using MFCC, spectral centroid, spectral flatness, spectral roll-off, spectral kurtosis, and zero crossing rate, reporting accuracies between 2% and 24%.The SVM, diverse density (DD), and expected maximization (EM) methods were implemented in [41] for the recognition of several sounds, including cutlery, water, voice, ambient, and music, using MFCC, spectral flux, spectral centroid, bandwidth, Normalized Mel-Frequency Bands, zero crossing rate, and low energy frame rate as features, reporting an average accuracy of 87%.In [42], several sounds were identified, including coffee machine brewing, hand washing, walking, elevator, door opening/closing, and silence, using k-Nearest Neighbour (k-NN), SVM and GMM methods with some features, such as zero crossing rate, short-time energy, temporal centroid, energy entropy, autocorrelation, RMS, spectral centroid, spectral spread, spectral roll-off point, spectral flux, spectral entropy, and MFCC methods. The highest accuracies achieved with the different methods are 97.9%, with k-NN, 90%, with GMM, and 100%, with SVM [42].The authors of [43] implemented the Random Forest, HMM, GMM, SVM, ANN, k-NN, and deep belief network methods in order to recognize babble, driving, machinery, crowded restaurant, street, air conditioner, washer, dryer, and vacuum cleaner, with MFCC, band periodicity, and band entropy, reporting results with a reliable accuracy.In [44], the authors implemented Naïve Bayes, k-NN, Random Forest, and Bayesian Networks methods for the recognition of several nursing activities, including measurement of height, patient sitting, assisting doctor, attaching/measuring/removing electrocardiography (ECG), changing bandage, cleaning body, examining edema, and washing hands, using several features, including mean of intensity, mean, variance of intensity, variance, mean of Fast Fourier Transform (FFT)-domain energy, and covariance between intensities.…”

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confidence: 99%

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2019

Stat., optim. inf. comput.

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The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.These methods are included in the development of a framework for the recognition of ADL and their environments, proposed in [5][6][7], composed by several modules, such as data acquisition, data processing, data fusion, and artificial intelligence methods. However, the data processing is composed by some steps, such as data cleaning and feature extraction, and the data fusion and artificial intelligence techniques are applied at the same time for the achievement of the final purpose of the recognition of ADL and their environments. The advantages of recognition of the environments are not limited to the increasing of the number of ADL recognized, but it allows the framework to combine the environments with the ADL recognition returning different results, e.g., the user is walking on the street.The topic related to the recognition of the ADL has some studies available in the literature [8][9][10][11][12][13], but there are no studies that uses all sensors available on the off-the-shelf mobile devices, however the Artificial Neural Networks (ANN) is one of the most used methods in this topic. Based on our previous studies using motion and magnetic sensors for the development of the framework for the recognition of ADL and their environments [4,14], this study proposes the creation of several methods to adapt the framework to the number of sensors available in off-the-shelf mobile devices. Some methods using different combinations of sensors are presented in previous studies [4,14], such as the method using accelerometer data, the meth...

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For Your Voice Only: Exploiting Side Channels in Voice Messaging for Environment Detection

Cardaioli

Conti

Ravindranath

2022

Computer Security – ESORICS 2022

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Using Smartphones to Classify Urban Sounds

Cited by 4 publications

References 16 publications

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

For Your Voice Only: Exploiting Side Channels in Voice Messaging for Environment Detection

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