MUSICAL SCALE IDENTIFICATION USING NEURAL NETWORKS

-Lyndon Quadros.

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.

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

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.

Octave -3Note Name Frequency Hz

C 32.70319566

C#/Db 34.64782887

D 36.70809599

D#/Eb 38.89087297

E 41.20344461

F 43.65352893

F#/Gb 46.24930284

G 48.9994295

G#/Ab 51.9130872

A 55

A#/Bb 58.27047019

B 61.73541266

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

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

Training Inputs

32.7032 36.7081 41.20344 46.2493 51.91309 55 61.73541

32.7032 36.7081 38.89087 43.65353 48.99943 55 58.27047

34.64783 38.89087 41.20344 46.2493 51.91309 58.27047 61.73541

32.7032 36.7081 41.20344 43.65353 48.99943 55 61.73541

32.7032 34.64783 38.89087 43.65353 46.2493 51.91309 58.27047

34.64783 36.7081 41.20344 46.2493 48.99943 55 61.73541

32.7032 36.7081 38.89087 43.65353 48.99943 51.91309 58.27047

34.64783 38.89087 41.20344 46.2493 51.91309 55 61.73541

32.7032 36.7081 41.20344 43.65353 48.99943 55 58.27047

34.64783 38.89087 43.65353 46.2493 51.91309 58.27047 61.73541

32.7032 36.7081 41.20344 46.2493 48.99943 55 61.7354

32.7032 34.64783 38.89087 43.65353 48.99943 51.91309 58.27047

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.

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.

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.

THANK YOU