Thesis Details
Codec Detection from Speech
This thesis deals with codec detection from compressed speech signal. The primary goal was to identify which features distinguish selected codecs, and then create an environment facilitating experiments with various types of classifiers and their configurations. Support vector machines and neural networks, modeled using the Keras library, were used. The main contribution of this work is the experimental part, in which the effects of the neural networks parameters are discussed. After tuning the parameters and finding their optimal values, the network achieved accuracy over 98% on a test set comprising data from six different codecs.
Neural networks, codec classification, speech processing, LPC, Keras, machine learning,Support vector machines, SVM, GRU, LSTM, codec
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT18356, author = "Josef Jon", type = "Bachelor's thesis", title = "Codec Detection from Speech", school = "Brno University of Technology, Faculty of Information Technology", year = 2017, location = "Brno, CZ", language = "english", url = "https://www.fit.vut.cz/study/thesis/18356/" }