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Open AccessArticle

Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification

Aholab Signal Processing Laboratory, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in IberSPEECH2018.
Appl. Sci. 2019, 9(15), 3140;
Received: 8 July 2019 / Revised: 29 July 2019 / Accepted: 30 July 2019 / Published: 2 August 2019
Speech and singing voice discrimination is an important task in the speech processing area given that each type of voice requires different information retrieval and signal processing techniques. This discrimination task is hard even for humans depending on the length of voice segments. In this article, we present an automatic speech and singing voice classification method using pitch parameters derived from musical note information and f 0 stability analysis. We applied our method to a database containing speech and a capella singing and compared the results with other discrimination techniques based on information derived from pitch and spectral envelope. Our method obtains good results discriminating both voice types, is efficient, has good generalisation capabilities and is computationally fast. In the process, we have also created a note detection algorithm with parametric control of the characteristics of the notes it detects. We compared the agreement of this algorithm with a state-of-the-art note detection algorithm and performed an experiment that proves that speech and singing discrimination parameters can represent generic information about the music style of the singing voice. View Full-Text
Keywords: audio segmentation; voice discrimination; singing voice; pitch audio segmentation; voice discrimination; singing voice; pitch
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Sarasola, X.; Navas, E.; Tavarez, D.; Serrano, L.; Saratxaga, I.; Hernaez, I. Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification. Appl. Sci. 2019, 9, 3140.

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