Learning Time-Series Similarity with a Neural Network by Combining Similarity Measures
Within this paper we present the approach of learning the non-linear combination of time-series similarity values through a neural network. A wide variety of time-series comparison methods, coefficients and criteria can be found in the literature that are all very specific, and hence apply only for a small fraction of applications. Instead of designing a new criteria we propose to combine the existing ones in an intelligent way by using a neural network. The approach aims to the goal of making the neural network to learn to compare the similarity between two time-series as a human would do. Therefore, we have implemented a set of comparison methods, the neural network and an extension to the learning rule to include a human as a teacher. First results are promising and show that the approach is valuable for learning human judged time-series similarity with a neural network.
KeywordsNeural Network Similarity Measure Hide Neuron Audio Signal Dynamic Time Warping
Unable to display preview. Download preview PDF.
- 1.Delgorge, C., Rosenberger, C., Poisson, G., Vieyres, P.: Evaluation of the Quality of Ultrasound Image Compression by Fusion of Critaria with a Genetic Algorithm. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 464–472. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 2.Aucouturier, J., Pachet, F.: Finding songs that sound the same. In: IEEE Benelux Workshop on Model Based Processing and Coding of Audio, Leuven, Belgium, pp. 91–98 (2002)Google Scholar
- 3.Aucouturier, J., Pachet, F.: Improving Timbre Similarity: How high i‘s the sky? Journal of Negative Research Results in Speech and Audio Science 1(1) (2004)Google Scholar
- 4.Berenzweig, A., Logan, B., Ellis D, P.W., Whitman, B.: A Large-Scale Evaluation of Acoustic and Subjective Music Similarity Measures. In: Third International Conference on Music Information Retrieval (ISMIR 2003), Washington DC (2003)Google Scholar
- 5.Prof. Dr. Clausen, M. Lecture: Multi Media Retrieval, Department of Computer Science. University of Bonn, Summer (2004)Google Scholar
- 6.Tzanetakis, G., Essl, G., Perry, C.: Automatic musical genre classification of audio signals, InternaGoogle Scholar
- 7.Tzanetakis, G., Perry, C.: Musical Genre Classification of Audio Signals. IEEE Transaction on Speech and Audio Processing 10(5) (July 2002)Google Scholar
- 9.Zell, A.: Simulation Neuronaler Netze. Oldenbourg Verlag, Munchen (2000)Google Scholar