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| Identifier |
000425807 |
| Title |
Assessing the quality of audio in musical concert recordings using deep neural networks |
| Alternative Title |
Εκτίμηση ποιότητας ηχογραφήσεων από μουσικές συναυλίες με χρήση τεχνικών βαθιάς μάθησης |
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Author
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Σίμου, Νίκων Χ.
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Thesis advisor
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Τσακαλίδης, Παναγιώτης
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Reviewer
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Στεφανάκης, Νίκος
Δημητρόπουλος, Ξενοφώντας
Πανταζής, Γιάννης
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| Abstract |
The era in which we live in can be indisputably characterized by the enormous flow of
multimedia information. Using portable multimedia devices such as drones and
smartphones we are able to capture every moment of our lives and of the public events
that we attend. A large proportion of audiovisual recordings from these events becomes
available through the social media and the large number of websites which provide video
and audio content. The availability of such massive amount of User Generated Recordings
(UGRs) has triggered new research directions related to the search, organization and
management of this content.
In this Thesis, we use Deep Neural Networks (DNN) in order to create a tool to
automatically assess the audio quality of musical concert recordings that users upload on
multimedia platforms such as YouTube. It is well known that DNNs require a lot of training
samples, which means that one would need an enormous amount of time in order to
listen and to assign a subjective quality score to each audio sample. We tackle this
problem by treating quality assessment as a binary classification problem where class 0
consist of the set of UGRs from a certain event and class 1 consist of the professional
quality recordings from the same event. Furthermore, we use an automatic
synchronization process in order to match every UGR with its corresponding segment
from a professional quality recording, which assists in making the process invariant to
audio content. Experiments produced with different DNN architectures and acoustic
feature are presented, showing that the UGR class can be discriminated from the
professional quality class with a high accuracy.
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| Language |
English |
| Subject |
Audio processing |
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Deep learning |
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Βαθειά μάθηση |
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Επεξεργασία ήχου |
| Issue date |
2019-11-22 |
| Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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| Views |
495 |
Assessing the quality of audio in musical concert recordings using deep neural networks
