4 research outputs found
Progressive Filtering Using Multiresolution Histograms for Query by Humming System
The rising availability of digital music stipulates effective categorization
and retrieval methods. Real world scenarios are characterized by mammoth music
collections through pertinent and non-pertinent songs with reference to the
user input. The primary goal of the research work is to counter balance the
perilous impact of non-relevant songs through Progressive Filtering (PF) for
Query by Humming (QBH) system. PF is a technique of problem solving through
reduced space. This paper presents the concept of PF and its efficient design
based on Multi-Resolution Histograms (MRH) to accomplish searching in
manifolds. Initially the entire music database is searched to obtain high
recall rate and narrowed search space. Later steps accomplish slow search in
the reduced periphery and achieve additional accuracy.
Experimentation on large music database using recursive programming
substantiates the potential of the method. The outcome of proposed strategy
glimpses that MRH effectively locate the patterns. Distances of MRH at lower
level are the lower bounds of the distances at higher level, which guarantees
evasion of false dismissals during PF. In due course, proposed method helps to
strike a balance between efficiency and effectiveness. The system is scalable
for large music retrieval systems and also data driven for performance
optimization as an added advantage.Comment: 12 Pages, 6 Figures, Full version of the paper published at
ICMCCA-2012 with the same title,
Link:http://link.springer.com/chapter/10.1007/978-81-322-1143-3_2
Development of a deep learning system for hummed melody identification for BertsoBot
The system introduced in this work tries to solve the problem of melody
classification. The proposed approach is based on extracting the spectrogram of the
audio of each melody and then using deep supervised learning approaches to classify
them into categories.
As found out experimentally, the Transfer Learning technique is required
alongside Data Augmentation in order to improve the accuracy of the system.
The results shown in this thesis, focus further work on this field by providing
insight on the performance of different tested Learning Models.
Overall, DenseNets have proved themselves the best architectures o use in
this context reaching a significant prediction accuracy
Development of a deep learning system for hummed melody identification for BertsoBot
The system introduced in this work tries to solve the problem of melody
classification. The proposed approach is based on extracting the spectrogram of the
audio of each melody and then using deep supervised learning approaches to classify
them into categories.
As found out experimentally, the Transfer Learning technique is required
alongside Data Augmentation in order to improve the accuracy of the system.
The results shown in this thesis, focus further work on this field by providing
insight on the performance of different tested Learning Models.
Overall, DenseNets have proved themselves the best architectures o use in
this context reaching a significant prediction accuracy