Two-step detection of water sound events for the diagnostic and monitoring of dementia

Guyot, Patrice; Pinquier, Julien; Valero, Xavier; Álías, Francesc

doi:10.1109/icme.2013.6607558

Cited by 14 publications

(13 citation statements)

References 16 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Systems performing sound event classification are usually evaluated in terms of accuracy [2,4,5,11,13,14]. Studies involving both monophonic and polyphonic sound event detection report results using a variety of metrics, for example Precision, Recall and F-score [6] or only F-score [7,26], recognition rate and false positive rate [3], or false positive and false negative rates [1]. One of the first evaluation campaigns for sound event detection (CLEAR 2007) used the acoustic event error rate as the evaluation metric, expressed as time percentage [29].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Metrics for Polyphonic Sound Event Detection

2016

View full text Add to dashboard Cite

This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.

show abstract

Section: Discussionmentioning

confidence: 99%

“…It has many applications in surveillance for security, healthcare and wildlife monitoring [1][2][3][4][5][6][7], and audio and video content based indexing and retrieval [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Metrics for Polyphonic Sound Event Detection

2016

View full text Add to dashboard Cite

show abstract

“…On the contrary, the latter enables recognition of the input acoustic data in a predefined closed set of acoustic classes or audio events, after enough representative data have been collected to train the classifier appropriately. The multi-class detection-by-classification AED has provided good results in different domains of application, such as ambient assisted living [ 21 , 22 ], birdsong recognition [ 23 , 24 ] or outdoor monitoring [ 25 , 26 , 27 ], to name a few. Moreover, the research on AED in urban environments has attracted much attention in recent years, mainly focused on surveillance applications, which may also include noise source identification [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

Socoró

Álías

Alsina-Pagès

2017

Sensors

View full text Add to dashboard Cite

One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement.

show abstract

“…In indoor applications, an approach to ambient assisted living purposes was conducted in [2] to help the diagnosis of the first stages of people with dementia. For outdoor applications, especially oriented to smartcity environments, we have detected types of vehicles [3], classified events in soundscapes [4] and even specifically in surveillance applications [5].…”

Section: Introductionmentioning

confidence: 99%

An FPGA Platform Proposal for Real-Time Acoustic Event Detection: Optimum Platform Implementation for Audio Recognition with Time Restrictions

Hervás¹,

Alsina-Pagès²

2016

Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Availabl

View full text Add to dashboard Cite

Nowadays, monitoring of people and events is a common matter in the street, in the industry or at home, and acoustic event detection is commonly used. This increases the knowledge of what is happening in the soundscape, and this information encourages any monitoring system to take decisions depending on the measured events. Our research in this field includes, on one hand, smart city applications, which aim is to develop a low cost sensor network for real time noise mapping in the cities, and on the other hand, ambient assisted living applications through audio event recognition at home. This requires acoustic signal processing for event recognition, which is a challenging problem applying feature extraction techniques and machine learning methods. Furthermore, when the techniques come closer to implementation, a complete study of the most suitable platform is needed, taking into account computational complexity of the algorithms and commercial platforms price. In this work, the comparative study of several platforms serving to implement this sensing application is detailed. An FPGA platform is chosen as the optimum proposal considering the application requirements and taking into account time restrictions of the signal processing algorithms. Furthermore, we describe the first approach to the real-time implementation of the feature extraction algorithm on the chosen platform.

show abstract

Two-step detection of water sound events for the diagnostic and monitoring of dementia

Cited by 14 publications

References 16 publications

Metrics for Polyphonic Sound Event Detection

Metrics for Polyphonic Sound Event Detection

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

An FPGA Platform Proposal for Real-Time Acoustic Event Detection: Optimum Platform Implementation for Audio Recognition with Time Restrictions

Contact Info

Product

Resources

About