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      • Record: found
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      A method for estimation of fundamental frequency for tonal sounds inspired on bird song studies

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      Publication date (Electronic):
      Journal: MethodsX
      Publisher: Elsevier
      Keywords: A maximum intensity of frequency decomposition method, Signal analysis, Fundamental frequency, Python code, Open source

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          Abstract

          In this work a simple implementation of fundamental frequency estimation is presented. The algorithm is based on a frequency-domain approach. It was mainly developed for tonal sounds and it was used in Canary birdsong analysis. The method was implemented but not restricted for this kind of data. It could be easily adapted for other sounds. Python libraries were used to develop a code with a simple algorithm to obtain fundamental frequency. An open source code is provided in the local university repository and Github.

          • •

            The algorithm and the implementation are very simple and cover a set of potential applications for signal analysis.

          • •

            Code implementation is written in python, very easy to use and modify.

          • •

            Present method is proposed to analyze data from sounds of Serinus canaria.

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          Most cited references11

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          YIN, a fundamental frequency estimator for speech and music.

          Alain de Cheveigné, Hideki Kawahara (2002)
          An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.
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            System for Automatic Formant Analysis of Voiced Speech

            Ronald Schafer, Lawrence R. Rabiner (1970)
            Article 0 comments Cited 41 times – based on 0 reviews     Review now
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              A Daily Oscillation in the Fundamental Frequency and Amplitude of Harmonic Syllables of Zebra Finch Song

              William Wood, Peter Osseward, Thomas K. Roseberry … (2013)
              Complex motor skills are more difficult to perform at certain points in the day (for example, shortly after waking), but the daily trajectory of motor-skill error is more difficult to predict. By undertaking a quantitative analysis of the fundamental frequency (FF) and amplitude of hundreds of zebra finch syllables per animal per day, we find that zebra finch song follows a previously undescribed daily oscillation. The FF and amplitude of harmonic syllables rises across the morning, reaching a peak near mid-day, and then falls again in the late afternoon until sleep. This oscillation, although somewhat variable, is consistent across days and across animals and does not require serotonin, as animals with serotonergic lesions maintained daily oscillations. We hypothesize that this oscillation is driven by underlying physiological factors which could be shared with other taxa. Song production in zebra finches is a model system for studying complex learned behavior because of the ease of gathering comprehensive behavioral data and the tractability of the underlying neural circuitry. The daily oscillation that we describe promises to reveal new insights into how time of day affects the ability to accomplish a variety of complex learned motor skills.
              Article 0 comments Cited 8 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Cecilia Jarne
                Journal
                Journal ID (nlm-ta): MethodsX
                Journal ID (iso-abbrev): MethodsX
                Title: MethodsX
                Publisher: Elsevier
                ISSN (Electronic): 2215-0161
                Publication date PMC-release: 04 January 2019
                Publication date Collection: 2019
                Publication date (Electronic): 04 January 2019
                Volume: 6
                Pages: 124-131
                Affiliations
                [0005]Universidad Nacional de Quilmes, Departamento de Ciencia y Tecnología – CONICET, Buenos Aires, Argentina
                Article
                Publisher ID: S2215-0161(18)30214-0
                DOI: 10.1016/j.mex.2018.12.011
                PMC ID: 6330366
                SO-VID: b186f965-eb46-431e-b7d4-13089320a72f
                Copyright © © 2018 The Author(s)
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 15 February 2018
                Date accepted : 20 December 2018
                Categories
                Subject: Computer Science

                Keywords: a maximum intensity of frequency decomposition method,signal analysis,fundamental frequency,python code,open source
                Data availability:
                Keywords: a maximum intensity of frequency decomposition method, signal analysis, fundamental frequency, python code, open source

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