1. MUSICAL SCALE IDENTIFICATION USING NEURAL NETWORKS -Lyndon Quadros.

2. Scales and Octaves Musical scale - (music) a series of notes differing in pitch according to a specific scheme (usually within an octave) Octave-a series of eight notes occupying the interval between (and including) two notes, one having twice or half the frequency of vibration of the other.

3. Which Scales and Which Octaves? Western classical musical scales. All majors and their relative minors. A/F#m, A#/Gm, B/G#m, C/Am, D/Bm, D#/Cm, E/C#m, F/Dm, F#/D#m, G/Em, G#/Fm 12 Scales in all i.e. 12 Output Classes

4. Which Scales and Which Octaves? Scales in all the 10 octaves can be classified. However, Octaves -3 to +5 are audible to human ear. All the notes obtained will be scaled (normalised) down to the -3 octave.

5. Octave -3 Note NameFrequency Hz C32.70319566 C # /D b 34.64782887 D36.70809599 D # /E b 38.89087297 E41.20344461 F43.65352893 F # /G b 46.24930284 G48.9994295 G # /A b 51.9130872 A55 A # /B b 58.27047019 B61.73541266

6. Feature Vectors Frequencies of the notes that are present in each scale. Western music defines 7 notes for each scale. Hence, the input feature vector will be a 7- dimensional vector. Frequencies are obtained by Pitch detection

7. Pitch and Pitch Detection Pitch : The degree of “highness” or “lowness” of a note. Can be quantified in terms of frequency or number of cents from a reference note. Tarsos_Yin pitch detection algorithm has been employed

9. Training Inputs 32.703236.708141.2034446.249351.913095561.73541 32.703236.708138.8908743.6535348.999435558.27047 34.6478338.8908741.2034446.249351.9130958.2704761.73541 32.703236.708141.2034443.6535348.999435561.73541 32.703234.6478338.8908743.6535346.249351.9130958.27047 34.6478336.708141.2034446.249348.999435561.73541 32.703236.708138.8908743.6535348.9994351.9130958.27047 34.6478338.8908741.2034446.249351.913095561.73541 32.703236.708141.2034443.6535348.999435558.27047 34.6478338.8908743.6535346.249351.9130958.2704761.73541 32.703236.708141.2034446.249348.999435561.7354 32.703234.6478338.8908743.6535348.9994351.9130958.27047

10. Testing Data 36 vectors of 7 features (All 12 scales in three different progressions) Pre-processed to obtain the frequencies and extract the 7 most frequently occurring frequencies from the pitch detection. Normalised to the -3 frequency and arranged.

11. Current Status Completed feature extraction algorithm. Pattern classification using MLP and back propagation algorithm for the current set of data gives a maximum classification rate of 10.33% with 2 hidden layers of 14 neurons each and learning rate 0.1 Lesser classification rate due to lower dimensionality of input as compared to output.

12. Got a Job to be Done Since the lower dimensionality hinders classification, Radial Basis Networks and SVM appear to be the best options. Success is also subject to accurate pitch detection. Hence, various different pitch detection algorithms to be tested.

13. THANK YOU