In this paper we introduce a Modular Audio Recognition Framework (MARF), as an open-source research platform implemented in Java. MARF is used to evaluate various pattern-recognition algorithms and beyond in areas such as audio and text processing (NLP) and may also act as a library in applications as well as serve as a basis for learning and extension as it encompasses good software engineering practices in its design and implementation. Thus, the paper subsequently summarizes the core framework’s features and capabilities and where it is heading.
Keywords
- Feature Extraction
- Natural Language Processing
- Pipeline Stage
- Linear Predictive Code
- Cosine Similarity Measure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Mokhov, I. Clement, S. Sinclair, and D. Nicolacopoulos, Modular Audio Recognition Framework. Department of Computer Science and Software Engineering, Concordia University, 2002-2003, http://marf.sf. net.
MARF Research & Development Group, Modular Audio Recognition Framework and Applications. SourceForge.net, 2002-2007, http://marf. sf.net.
S. Mokhov, On Design and Implementation of Distributed Modular Audio Recognition Framework: Requirements and Specification Design Document. Department of Computer Science and Software Engineering, Concordia University, 2006, http://marf.sf.net.
S. A. Mokhov, “Towards Hybrid Intensional Programming with JLucid, Objective Lucid, and General Imperative Compiler Framework in the GIPSY,” Master’s thesis, Department of Computer Science and Software Engineering, Concordia University, Oct. 2005, iSBN 0494102934.
T. G. Research and D. Group, The GIPSY Project. Department of Computer Science and Software Engineering, Concordia University, 2002-2007, http://newton.cs.concordia.ca/gipsy/.
A. Wollrath and J. Waldo, Java RMI Tutorial. Sun Microsystems, Inc., 1995-2005, http://java.sun.com/docs/books/tutorial/rmi/index.html.
S. Microsystems, Java IDL. Sun Microsystems, Inc., 2004, http://java. sun.com/j2se/1.5.0/docs/guide/idl/index.html.
S. Microsystems, The Java Web Services Tutorial (For Java Web Services Developer’s Pack, v2.0). Sun Microsystems, Inc., Feb. 2006, http: //java.sun.com/webservices/docs/2.0/tutorial/doc/index.html.
S. Mokhov, “MARF Coding Conventions,” 2005-2007, http://marf.sf. net/coding.html.
S. M. Bernsee, The DFT “a pied": Mastering The Fourier Transform in One Day. DSPdimension.com, 1999-2005, http://www.dspdimension. com/data/html/dftapied.html.
H. Abdi, “Distance.” In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage, 2007, http://en. wikipedia.org/wiki/Chebyshev distance.
H. Abdi, “Distance.” In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage, 2007, http://en.wikipedia.org/ wiki/Euclidean distance.
P. Mahalanobis, “On the generalised distance in statistics.” Proceedings of the National Institute of Science of India 12 (1936) 49-55, 1936, http://en.wikipedia.org/wiki/Mahalanobis distance.
R. W. Hamming, “Error Detecting and Error Correcting Codes.” Bell System Technical Journal 26(2):147-160, 1950, http://en.wikipedia.org/ wiki/Hamming distance.
H. Abdi, “Distance." In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage, 2007, http://en. wikipedia.org/wiki/Distance#Distance in Euclidean space.
E. Garcia, “Cosine similarity and term weight tutorial,” 2006, http://www.miislita.com/information-retrieval-tutorial/ cosine-similarity-tutorial.html.
A. Kishore, “Similarity measure: Cosine similarity or euclidean distance or both,” feb 2007, http://semanticvoid.com/blog/2007/02/23/ similarity-measure-cosine-similarity-or-euclidean-distance-or-both/.
G. K. Zipf, The Psychobiology of Language. Houghton-Miffin, New York, NY, 1935, http://en.wikipedia.org/wiki/Zipf%27s law.
S. Haridas, “Generation of 2-d digital filters with variable magnitude characteristics starting from a particular type of 2-variable continued fraction expansion,” Master’s thesis, Concordia University, Montr’eal, Canada, Jul. 2006.
J. H. Martin, CYK Probabilistic Parsing Algorithm, http://www.cs. colorado.edu/martin/SLP/New Pages/pg455.pdf.
E. Gamma and K. Beck, JUnit. Object Mentor, Inc., 2001-2004, http: //junit.org/.
T. S. G. at Carnegie Mellon, The CMU Sphinx Group Open Source Speech Recognition Engines. cmusphinx.org, 2007, http://cmusphinx. sourceforge.net.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this paper
Cite this paper
Mokhov, S.A. (2008). Introducing MARF: a Modular Audio Recognition Framework and its Applications for Scientific and Software Engineering Research. In: Sobh, T. (eds) Advances in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8741-7_84
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8741-7_84
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8740-0
Online ISBN: 978-1-4020-8741-7
eBook Packages: Computer ScienceComputer Science (R0)