Acoustic Hazard Detection for Pedestrians With Obscured Hearing

Pedestrians' use of Motion Pictures Expert Group audio layer 3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel simple nonresource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three-component heuristic. The resulting acoustic hazard detection system has a very low false-positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.

[1]  Anssi Klapuri,et al.  Signal Processing Methods for Music Transcription , 2006 .

[2]  Augusto Sarti,et al.  Scream and gunshot detection and localization for audio-surveillance systems , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[3]  Mingchun Liu,et al.  A study on content-based classification and retrieval of audio database , 2001, Proceedings 2001 International Database Engineering and Applications Symposium.

[4]  David Gerhard,et al.  Audio Signal Classification: History and Current Techniques , 2003 .

[5]  Sigeru Omatu,et al.  Classification of bill fatigue levels by feature-selected acoustic energy pattern using competitive neural network , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[6]  John S. Baras.  Sound Classification and Localization Based on Biology Hearing Models and Multiscale Vector Quantization , 1999 .

[7]  Selina Chu,et al.  Unstructured Audio Classification for Environment Recognition , 2008, AAAI.

[8]  Antti Eronen,et al.  Comparison of features for musical instrument recognition , 2001, Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics (Cat. No.01TH8575).

[9]  Stefan Launer,et al.  Automatic Sound Classification Inspired by Auditory Scene Analysis , 2001 .

[10]  Q Carseldine,et al.  CENTRE FOR ACCIDENT RESEARCH & ROAD SAFETY - QUEENSLAND , 2004 .

[11]  Da Deng,et al.  Finding the Right Features for Instrument Classification of Classical Music , 2006, 2006 International Workshop on Integrating AI and Data Mining.

[12]  Jeroen Breebaart,et al.  Features for audio and music classification , 2003, ISMIR.

[13]  Andrey Temko,et al.  Classification of acoustic events using SVM-based clustering schemes , 2006, Pattern Recognit..

[14]  Preeti Rao,et al.  AUDIO SIGNAL CLASSIFICATION , 2004 .

[15]  Michael A. Cowling,et al.  Non-Speech Environmental Sound Classification System for Autonomous Surveillance , 2004 .

[16]  Roberto Togneri,et al.  SPECTRAL ENTROPY AS SPEECH FEATURES FOR SPEECH RECOGNITION , 2005 .

[17]  Jouni Pohjalainen,et al.  Methods of automatic audio content classification , 2007 .

[18]  Bruce Corben,et al.  Estimating pedestrian fatal crash risk , 2006 .

[19]  Jouni Paulus,et al.  Unpitched Percussion Transcription , 2006 .

[20]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[21]  J C Brown,et al.  Feature dependence in the automatic identification of musical woodwind instruments. , 2001, The Journal of the Acoustical Society of America.

[22]  Renate Sitte,et al.  Comparison of techniques for environmental sound recognition , 2003, Pattern Recognit. Lett..

[23]  Shrikanth Narayanan,et al.  Environmental Sound Recognition With Time–Frequency Audio Features , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[24]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization using ambient sound and light , 2009, MOCO.