A statistical pattern recognition approach for the classification of cooking stages. The boiling water case

Abstract Although pattern recognition technique has been widely used in many fields, it seems that very few studies have applied this technique to cooking processes. In this preliminary research, a new methodology has been developed and tested on a simple case of water boiling. Besides defining and analysing the efficacy and the performance of a statistical pattern recognition approach when applied to different signals (sound and vibration), an optimisation module has been proposed to boost the classification rates by adding syntactical analysis that enables the inertia of the process to be considered. In the specific case of boiling water, almost 100% successful recognition has been reached. These results prove the validity of this methodology, opening up new research lines for new scenarios, such as different cooking processes, acoustically polluted environments, and sensor optimisation.

[1]  George Daniel Doney.  Acoustic boiling detection , 1994 .

[2]  Francis F. Li,et al.  A neural network model for speech intelligibility quantification , 2007, Appl. Soft Comput..

[3]  Goutam Saha,et al.  Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition , 2012, Speech Commun..

[4]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[5]  Manuel Recuero,et al.  Real-time aircraft noise likeness detector , 2010 .

[6]  Philip N. Garner.  Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition , 2011, Speech Commun..

[7]  Shantanu Chakrabartty,et al.  An Overview of Statistical Pattern Recognition Techniques for Speaker Verification , 2011, IEEE Circuits and Systems Magazine.

[8]  Martin A. Kraaijveld,et al.  A Parzen classifier with an improved robustness against deviations between training and test data , 1996, Pattern Recognit. Lett..

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

[10]  J. Westwater,et al.  Sound of Boiling. , 1955, Science.

[11]  Miha Mraz,et al.  Towards automated cooking process , 2007 .

[12]  Mohammad Farhan,et al.  Support Vector Machine Classifier for Pattern Recognition , 2011, 2011 First International Conference on Informatics and Computational Intelligence.

[13]  Alfred Mertins,et al.  Automatic speech recognition and speech variability: A review , 2007, Speech Commun..

[14]  Chungyong Lee,et al.  Optimizing feature extraction for speech recognition , 2003, IEEE Trans. Speech Audio Process..

[15]  Vincent Fontaine,et al.  AUTOMATIC CLASSIFICATION OF ENVIRONMENTAL NOISE EVENTS BY HIDDEN MARKOV MODELS , 1998 .

[16]  Yang Zhang,et al.  Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network , 2011, Adv. Robotics.

[17]  Ye. I. Nesis.  Acoustic noise of a boiling liquid , 1990 .