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            Export Date: 8 September 2022</comment>
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      <abstractText>The main aim of the presented paper is to create a web portal, which allows the user to reckognize the sequence of chords in the song. To recognize chords from an audio recording, it is necessary to use input signal processing methods such as constant Q-transform or harmonic pitch class profile. A categorization template is created for each supported chord that maximizes the match between the processed signal and the chord. Techniques such as temporal and spectral smoothing or detection of harmonic changes were used for preprocessing. Based on the evaluation methods, the best classification strategy was selected, whose success prediction accuracy reaches 75 percent. Success has been achieved through the use of machine learning techniques. The system is publicly available on the public domain for anyone interested in this type of service. © 2022 IEEE.</abstractText>
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          <label>1. Madoš, B., Adam, N., Bilanova, Z., Chovanec, M., FPGA HW Accelerator of the First Step of Systematic Two-Level Minimization of Single-Output Boolean Function (2020) Acta Polytechnica Hungarica, 17 (7), pp. 25-43</label>
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          <label>2. Muller, M., Fundamentals of Music Processing (2016) Audio, Analysis, Algorithms, Applications, , Springer International Publishing</label>
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          <label>3. Kopakova, S., Uvod do študia dejin hudby pre študentov estetiky (2014) Prešovska univerzita v prešove</label>
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          <label>4. Harte, C., (2010) Towards automatic extraction of harmony information from music signals, , Queen Mary University of London, jan</label>
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          <label>5. (2009) Centre for Digital Music Queen Mary University of London, OMRAS2 Metadata Project 2009, , https://www.eecs.qmul.ac.uk/~simond/pub/2009/late-breakingC4DM.pdf</label>
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          <label>6. Sejdic, E., Djurovic, I., Jiang, J., Time-frequency feature representation using energy concentration: An overview of recent advances (2009) Digital Signal Processing, 19 (1), pp. 153-183. , https://doi.org/10.1016/j.dsp.2007.12.004</label>
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          <label>9. Dankoviova, Z., Sovak, D., Drotar, P., Vokorokos, L., Machine learning approach to dysphonia detection (2018) Applied Sciences, 8 (10)</label>
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          <label>10. Harte, C., Sandle, M., (2005) Automatic Chord Identification Using a Quantised Chromagram, , Queen Mary University of London, maj</label>
          <listPosition>10</listPosition>
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          <link>/api/reference/33445851</link>
          <label>11. Harte, C., (2005) Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations, pp. 66-71. , Queen Mary University of London, jan</label>
          <listPosition>11</listPosition>
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          <label>12. Fujishim, T., Realtime chord recognition of musical sound: A system using common lisp music (1999) ICMC, pp. 464-467</label>
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          <label>13. Droppova, A., Zaklady hudobnej teorie: Uebny text pre odbor uitestvo pre 1. st. ZŠ (1998) Vysokoškolske uebne texty / Pedagogicka fakulta Prešovskej univerzity. Prešovska univerzita</label>
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          <label>14. Brown, J., Calculation of a Constant Q Spectral Transform (1991) Journal of the Acoustical Society of America, 89, p. 425. , jan</label>
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          <published>false</published>
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          <label>15. Mitchell, D.P., Netravali, A.N., Reconstruction Filters in ComputerGraphics (1988) Proceedings of the 15th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH '88, pp. 221-228. , New York, NY, USA: Association for Computing Machinery</label>
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          <label>16. Young, R.W., Terminology for Logarithmic Frequency Units (1939) The Journal of the Acoustical Society of America, 11 (1), pp. 134-139. , https://doi.org/10.1121/1.1916017, jul</label>
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          <published>false</published>
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          <label>17. Vokorokos, L., Pekar, A., Adam, N., Yet Another Attempt in User Authentication (2013) Acta polytechnica hungarica, 10 (3), pp. 37-50</label>
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          <label>18. Harte, C., Sandler, M., Gasser, M., (2006) Detecting harmonic change in musical audio, , Queen Mary University of London, okt</label>
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          <label>19. Gnip, P., Vokorokos, L., Drotar, P., Selective oversampling approach for strongly imbalanced data. (2021) PeerJ Computer Science, 7, p. e604</label>
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          <label>21. Juhar, J., Vokorokos, L., Separation of Concerns and Concern Granularity in Source Code (2015) IEEE 13th International Scientific Conference on Informatics ? Informatics, , November 18-20, Poprad, Slovakia</label>
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          <published>false</published>
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2025.09.18. 12:08 Andódy Katalin (BME admin4)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: IEEE Control Systems Chapter, Hungary; IEEE Hungary Section; IEEE Joint Chapter of IES and RAS, Hungary; IEEE SMC Chapter, Hungary            
            Conference code: 179566            
            Export Date: 8 September 2022&lt;/pre&gt;

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