Real-Time Hardware Implementation of a Sound Recognition System with In-Field Learning

Mauricio KUGLER  Teemu TOSSAVAINEN  Miku NAKATSU  Susumu KUROYANAGI  Akira IWATA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.7   pp.1885-1894
Publication Date: 2016/07/01
Publicized: 2016/03/30
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7432
Type of Manuscript: PAPER
Category: Speech and Hearing
Keyword: 
environmental sound recognition,  binary features,  field-programmable gate arrays,  in-field learning,  

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Summary: 
The development of assistive devices for automated sound recognition is an important field of research and has been receiving increased attention. However, there are still very few methods specifically developed for identifying environmental sounds. The majority of the existing approaches try to adapt speech recognition techniques for the task, usually incurring high computational complexity. This paper proposes a sound recognition method dedicated to environmental sounds, designed with its main focus on embedded applications. The pre-processing stage is loosely based on the human hearing system, while a robust set of binary features permits a simple k-NN classifier to be used. This gives the system the capability of in-field learning, by which new sounds can be simply added to the reference set in real-time, greatly improving its usability. The system was implemented in an FPGA based platform, developed in-house specifically for this application. The design of the proposed method took into consideration several restrictions imposed by the hardware, such as limited computing power and memory, and supports up to 12 reference sounds of around 5.3 s each. Experimental results were performed in a database of 29 sounds. Sensitivity and specificity were evaluated over several random subsets of these signals. The obtained values for sensitivity and specificity, without additional noise, were, respectively, 0.957 and 0.918. With the addition of +6 dB of pink noise, sensitivity and specificity were 0.822 and 0.942, respectively. The in-field learning strategy presented no significant change in sensitivity and a total decrease of 5.4% in specificity when progressively increasing the number of reference sounds from 1 to 9 under noisy conditions. The minimal signal-to-noise ration required by the prototype to correctly recognize sounds was between -8 dB and 3 dB. These results show that the proposed method and implementation have great potential for several real life applications.