MUSICAL SCALE IDENTIFICATION USING NEURAL NETWORKS

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

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

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