College of Engineering Pune
Shivajinagar, Pune-411005. INDIA
Dr. S. P. Mahajan
Associate Professor,
Department of Electronics & Telecommunication
E-mail: spm.extc@coep.ac.in;
Ph. No. 91-20-25507141
Ph No: +919423005235(M)
Name of the candidate:
Degree Registered:
Title of Thesis:
Guide:
PhD Thesis Evaluation Report
Ms. Surekha Puri
Doctor of Philosophy (PhD) in Electronics and Communication Engineering,
Savitribai Phule Pune University, Pune
A Neural Network Based Automatic Harmonium Music Transcription
Prof. S. P. Mahajan
General Observations:
The candidate has selected a challenging research problem of A Neural Network Based Automatic
Harmonium Music Transcription for her PhD thesis which is very much useful for automatic transcription
of music for harmonium. The candidate has made a detailed research report organized into seven chapters.
The candidate has mentioned 86 references that she has used for her research. The main aim of this thesis is
to design the automatic music transcription system for harmonium using neural networks. In order to
achieve this aim objectives the candidate has chosen following objectives:1. To the creation of an exhaustive database covering a wide range of harmonium recordings in Indian
Classical music.
2. To identification of suitable parameters for Harmonium notes transcription and suggesting neural
network-based acoustic model as well as polyphonic model.
3. To maximization of transcription accuracy of Harmonium notes and chords by incorporating neural
network based on Music Language Model.
4. To the development of a novel neural network model that will be a combination of acoustic model
and Music Language Model for yielding good accuracies.
5. To formulation of a suitable performance evaluation metric for the Harmonium music transcription
system.
The candidate has conducted an enough experimentation to meet the above objectives. The thesis is well
organized and very well written with clarity of thought. The main contributions of this research work can be
summarized as follows:
1. Dataset creation of notes, chords, and triads of Harmonium.
2. Identification of particular notes for the Acoustic model(AM)and chord for the Music language model
(MLM)for Harmonium music transcription using Convolutional neural network (CNN) and Recurrent
Neural Network(RNN).
3. Hybrid model development for harmonium transcription using the Convolutional Recurrent Neural
Network(CRNN) model.
4. Conversion of recognized notes and chords into sheet music representation using the lilypond library.
5. Implementation and designing of GUI for AMT are done and which is useful for new learners in the
domain of music transcription for the notes and chords transcriptions.
Designing of AMT model for monophonic using CNN and RNN approach is well designed for detecting
the pitch frequency of a particular note. Various feature extraction techniques such as pitch frequency,
MFCC, LPC,CQT and Statistical data for audio notes were implemnted using feed forward neural
Networks.
The same model is designed for polyphonic (MLM) approach using CNN and RNN . Frequency plot of the
detected chords from the corresponding octive is optimised and also sheet music representation is plotted
using lilypond library.
Hybrid model for harmonium music transcription using the chroma CENS functions from librosa library as
input CRNN model is implemented to improve the prediction accuracy of notes and chords.
Publications:
The candidate has total five publications, including two international conferences and three international journal
publications to her credit.
Recommendations:
Taking into consideration the above observations, it is clear that the thesis submitted by the candidate for her
PhD degree has made a good contribution to the knowledge in the broad area of “A Neural Network Based
Automatic Harmonium Music Transcription”. The candidate deserves to be complimented for taking up
contemporary research topic, conducting the exhaustive experimentation and coming out with useful results.
I recommend that the thesis submitted by Ms.Puri Surekha B. be accepted for the award of the Degree of Doctor
of Philosophy of Savitribai Phule Pune University, Pune.
Pune
Date: 29th August 2023
Dr. S. P. Mahajan