1. Classification of Music According to Genres Using Neural Networks, Genetic Algorithms and Fuzzy Systems

2. Progress Presentation by: Asma Amir 05020221 Nazia Zaman 05020247

3. Architecture of System Feature Extraction Pre processor Neural Network + Fuzzy Systems Wav file Feature vector Pre processed frame vector Classification vector Genetic Algorith m

4. Music Genres Identified by the System The classification system analyzes the contents of a.wav music file in order to sort it into specific categories: classical, jazz, country, rap, punk, and techno.

5. Feature Extraction In order to classify music samples, the characteristics in both the time and frequency domains must be examined: 1.bandwidth 2.beat(tempo) variability 3.high pass filtering 4.number of FFT coefficients above threshold 5.power spectral density 6.smoothness in frequency domain 7.total power

6. AUDIO feature extraction is a necessary step for the classification tasks. Size of the music file Handling by the Neural Network Feature Extraction Function

7. Feature Extraction (contd) For feature extraction we plan to map music into a semantic space consisting of a feature vector, which could include various aspects such as Frequency Content, Frequency coefficients and volume or any other musically meaningful category.

8. Feature Extraction (contd) Frequency content is found to be an important feature for classifying music. Three different frequency content calculations can be included in the feature vectors. The first feature being amplitude values of the DFT of the signal. The second calculation can be to take the natural logarithm amplitude values. The final calculation can be to take the inverse DFT of the logarithm of the amplitude. The volume of a musical piece is easily calculated as the variance of the samples.

9. Structure of Neural Network After research, a three- layer back propagation neural network has been found to be most suitable.

11. Structure of Neural Network (cont) The processing is done by multiple, weighted layers of nodes. Each node is connected to every node in the next layer, and at each interface between nodes are connecting fibers weighted by a sum.

12. Structure of Neural Network (cont) The nodes at each successive layer sum their inputs, weight the sum, and produce an output. The output of the final layer is the output of the system. In this manner, an output is predicted given an input.

13. Structure of Neural Network (cont) The training determines the weighting on the nodes. For example, we train the neural network by giving it the vectors of signal processing data (bandwidth, power spectral density, etc.) as well as the corresponding classification of music. E.g: Classical music is denoted as [1 0 0 0 0 0], jazz is denoted as [0 1 0 0 0 0], etc., as shifted delta functions.

14. Structure of Neural Network (cont) The neural network takes the input and feeds it through the system, evaluating the output. It then changes the weights in order to get a more accurate output. It continues to run the inputs through the network multiple times until the error between its output and the output you gave it is below a defined tolerance level.

15. The value along each dimension of the vector space is computed as an output from a pattern classifier which is trained to measure a particular feature. For this an M Neural Network can be trained to recognize membership in the M feature classes.